loading-3-or-more-data-sets-and-making-them-into-one.R
xholi
2020-12-08
setwd("C:\\Users\\xholi\\OneDrive\\Desktop")
##this will print a messy previe of the data sets##
readLines("ratings.csv", n= 7)## [1] "userId,movieId,rating,timestamp" "1,110,1.0,1425941529"
## [3] "1,147,4.5,1425942435" "1,858,5.0,1425941523"
## [5] "1,1221,5.0,1425941546" "1,1246,5.0,1425941556"
## [7] "1,1968,4.0,1425942148"readLines("ratings_small.csv" , n=7)## [1] "userId,movieId,rating,timestamp" "1,31,2.5,1260759144"
## [3] "1,1029,3.0,1260759179" "1,1061,3.0,1260759182"
## [5] "1,1129,2.0,1260759185" "1,1172,4.0,1260759205"
## [7] "1,1263,2.0,1260759151"readLines("credits.csv", n=7) #id## [1] "cast,crew,id"
## [2] "\"[{'cast_id': 14, 'character': 'Woody (voice)', 'credit_id': '52fe4284c3a36847f8024f95', 'gender': 2, 'id': 31, 'name': 'Tom Hanks', 'order': 0, 'profile_path': '/pQFoyx7rp09CJTAb932F2g8Nlho.jpg'}, {'cast_id': 15, 'character': 'Buzz Lightyear (voice)', 'credit_id': '52fe4284c3a36847f8024f99', 'gender': 2, 'id': 12898, 'name': 'Tim Allen', 'order': 1, 'profile_path': '/uX2xVf6pMmPepxnvFWyBtjexzgY.jpg'}, {'cast_id': 16, 'character': 'Mr. Potato Head (voice)', 'credit_id': '52fe4284c3a36847f8024f9d', 'gender': 2, 'id': 7167, 'name': 'Don Rickles', 'order': 2, 'profile_path': '/h5BcaDMPRVLHLDzbQavec4xfSdt.jpg'}, {'cast_id': 17, 'character': 'Slinky Dog (voice)', 'credit_id': '52fe4284c3a36847f8024fa1', 'gender': 2, 'id': 12899, 'name': 'Jim Varney', 'order': 3, 'profile_path': '/eIo2jVVXYgjDtaHoF19Ll9vtW7h.jpg'}, {'cast_id': 18, 'character': 'Rex (voice)', 'credit_id': '52fe4284c3a36847f8024fa5', 'gender': 2, 'id': 12900, 'name': 'Wallace Shawn', 'order': 4, 'profile_path': '/oGE6JqPP2xH4tNORKNqxbNPYi7u.jpg'}, {'cast_id': 19, 'character': 'Hamm (voice)', 'credit_id': '52fe4284c3a36847f8024fa9', 'gender': 2, 'id': 7907, 'name': 'John Ratzenberger', 'order': 5, 'profile_path': '/yGechiKWL6TJDfVE2KPSJYqdMsY.jpg'}, {'cast_id': 20, 'character': 'Bo Peep (voice)', 'credit_id': '52fe4284c3a36847f8024fad', 'gender': 1, 'id': 8873, 'name': 'Annie Potts', 'order': 6, 'profile_path': '/eryXT84RL41jHSJcMy4kS3u9y6w.jpg'}, {'cast_id': 26, 'character': 'Andy (voice)', 'credit_id': '52fe4284c3a36847f8024fc1', 'gender': 0, 'id': 1116442, 'name': 'John Morris', 'order': 7, 'profile_path': '/vYGyvK4LzeaUCoNSHtsuqJUY15M.jpg'}, {'cast_id': 22, 'character': 'Sid (voice)', 'credit_id': '52fe4284c3a36847f8024fb1', 'gender': 2, 'id': 12901, 'name': 'Erik von Detten', 'order': 8, 'profile_path': '/twnF1ZaJ1FUNUuo6xLXwcxjayBE.jpg'}, {'cast_id': 23, 'character': 'Mrs. Davis (voice)', 'credit_id': '52fe4284c3a36847f8024fb5', 'gender': 1, 'id': 12133, 'name': 'Laurie Metcalf', 'order': 9, 'profile_path': '/unMMIT60eoBM2sN2nyR7EZ2BvvD.jpg'}, {'cast_id': 24, 'character': 'Sergeant (voice)', 'credit_id': '52fe4284c3a36847f8024fb9', 'gender': 2, 'id': 8655, 'name': 'R. Lee Ermey', 'order': 10, 'profile_path': '/r8GBqFBjypLUP9VVqDqfZ7wYbSs.jpg'}, {'cast_id': 25, 'character': 'Hannah (voice)', 'credit_id': '52fe4284c3a36847f8024fbd', 'gender': 1, 'id': 12903, 'name': 'Sarah Freeman', 'order': 11, 'profile_path': None}, {'cast_id': 27, 'character': 'TV Announcer (voice)', 'credit_id': '52fe4284c3a36847f8024fc5', 'gender': 2, 'id': 37221, 'name': 'Penn Jillette', 'order': 12, 'profile_path': '/zmAaXUdx12NRsssgHbk1T31j2x9.jpg'}]\",\"[{'credit_id': '52fe4284c3a36847f8024f49', 'department': 'Directing', 'gender': 2, 'id': 7879, 'job': 'Director', 'name': 'John Lasseter', 'profile_path': '/7EdqiNbr4FRjIhKHyPPdFfEEEFG.jpg'}, {'credit_id': '52fe4284c3a36847f8024f4f', 'department': 'Writing', 'gender': 2, 'id': 12891, 'job': 'Screenplay', 'name': 'Joss Whedon', 'profile_path': '/dTiVsuaTVTeGmvkhcyJvKp2A5kr.jpg'}, {'credit_id': '52fe4284c3a36847f8024f55', 'department': 'Writing', 'gender': 2, 'id': 7, 'job': 'Screenplay', 'name': 'Andrew Stanton', 'profile_path': '/pvQWsu0qc8JFQhMVJkTHuexUAa1.jpg'}, {'credit_id': '52fe4284c3a36847f8024f5b', 'department': 'Writing', 'gender': 2, 'id': 12892, 'job': 'Screenplay', 'name': 'Joel Cohen', 'profile_path': '/dAubAiZcvKFbboWlj7oXOkZnTSu.jpg'}, {'credit_id': '52fe4284c3a36847f8024f61', 'department': 'Writing', 'gender': 0, 'id': 12893, 'job': 'Screenplay', 'name': 'Alec Sokolow', 'profile_path': '/v79vlRYi94BZUQnkkyznbGUZLjT.jpg'}, {'credit_id': '52fe4284c3a36847f8024f67', 'department': 'Production', 'gender': 1, 'id': 12894, 'job': 'Producer', 'name': 'Bonnie Arnold', 'profile_path': None}, {'credit_id': '52fe4284c3a36847f8024f6d', 'department': 'Production', 'gender': 0, 'id': 12895, 'job': 'Executive Producer', 'name': 'Ed Catmull', 'profile_path': None}, {'credit_id': '52fe4284c3a36847f8024f73', 'department': 'Production', 'gender': 2, 'id': 12896, 'job': 'Producer', 'name': 'Ralph Guggenheim', 'profile_path': None}, {'credit_id': '52fe4284c3a36847f8024f79', 'department': 'Production', 'gender': 2, 'id': 12897, 'job': 'Executive Producer', 'name': 'Steve Jobs', 'profile_path': '/mOMP3SwD5qWQSR0ldCIByd3guTV.jpg'}, {'credit_id': '52fe4284c3a36847f8024f8b', 'department': 'Editing', 'gender': 2, 'id': 8, 'job': 'Editor', 'name': 'Lee Unkrich', 'profile_path': '/bdTCCXjgOV3YyaNmLGYGOxFQMOc.jpg'}, {'credit_id': '52fe4284c3a36847f8024f91', 'department': 'Art', 'gender': 2, 'id': 7883, 'job': 'Art Direction', 'name': 'Ralph Eggleston', 'profile_path': '/uUfcGKDsKO1aROMpXRs67Hn6RvR.jpg'}, {'credit_id': '598331bf925141421201044b', 'department': 'Editing', 'gender': 2, 'id': 1168870, 'job': 'Editor', 'name': 'Robert Gordon', 'profile_path': None}, {'credit_id': '5892168cc3a36809660095f9', 'department': 'Sound', 'gender': 0, 'id': 1552883, 'job': 'Foley Editor', 'name': 'Mary Helen Leasman', 'profile_path': None}, {'credit_id': '5531824d9251415289000945', 'department': 'Visual Effects', 'gender': 0, 'id': 1453514, 'job': 'Animation', 'name': 'Kim Blanchette', 'profile_path': None}, {'credit_id': '589215969251412dcb009bf6', 'department': 'Sound', 'gender': 0, 'id': 1414182, 'job': 'ADR Editor', 'name': 'Marilyn McCoppen', 'profile_path': None}, {'credit_id': '589217099251412dc500a018', 'department': 'Sound', 'gender': 2, 'id': 7885, 'job': 'Orchestrator', 'name': 'Randy Newman', 'profile_path': '/w0JzfoiM25nrnxYOzosPHRq6mlE.jpg'}, {'credit_id': '5693e6b29251417b0e0000e3', 'department': 'Editing', 'gender': 0, 'id': 1429549, 'job': 'Color Timer', 'name': 'Dale E. 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## [2] "862,\"[{'id': 931, 'name': 'jealousy'}, {'id': 4290, 'name': 'toy'}, {'id': 5202, 'name': 'boy'}, {'id': 6054, 'name': 'friendship'}, {'id': 9713, 'name': 'friends'}, {'id': 9823, 'name': 'rivalry'}, {'id': 165503, 'name': 'boy next door'}, {'id': 170722, 'name': 'new toy'}, {'id': 187065, 'name': 'toy comes to life'}]\""
## [3] "8844,\"[{'id': 10090, 'name': 'board game'}, {'id': 10941, 'name': 'disappearance'}, {'id': 15101, 'name': \"\"based on children's book\"\"}, {'id': 33467, 'name': 'new home'}, {'id': 158086, 'name': 'recluse'}, {'id': 158091, 'name': 'giant insect'}]\""
## [4] "15602,\"[{'id': 1495, 'name': 'fishing'}, {'id': 12392, 'name': 'best friend'}, {'id': 179431, 'name': 'duringcreditsstinger'}, {'id': 208510, 'name': 'old men'}]\""
## [5] "31357,\"[{'id': 818, 'name': 'based on novel'}, {'id': 10131, 'name': 'interracial relationship'}, {'id': 14768, 'name': 'single mother'}, {'id': 15160, 'name': 'divorce'}, {'id': 33455, 'name': 'chick flick'}]\""
## [6] "11862,\"[{'id': 1009, 'name': 'baby'}, {'id': 1599, 'name': 'midlife crisis'}, {'id': 2246, 'name': 'confidence'}, {'id': 4995, 'name': 'aging'}, {'id': 5600, 'name': 'daughter'}, {'id': 10707, 'name': 'mother daughter relationship'}, {'id': 13149, 'name': 'pregnancy'}, {'id': 33358, 'name': 'contraception'}, {'id': 170521, 'name': 'gynecologist'}]\""
## [7] "949,\"[{'id': 642, 'name': 'robbery'}, {'id': 703, 'name': 'detective'}, {'id': 974, 'name': 'bank'}, {'id': 1523, 'name': 'obsession'}, {'id': 3713, 'name': 'chase'}, {'id': 7281, 'name': 'shooting'}, {'id': 9727, 'name': 'thief'}, {'id': 9812, 'name': 'honor'}, {'id': 9826, 'name': 'murder'}, {'id': 9937, 'name': 'suspense'}, {'id': 10051, 'name': 'heist'}, {'id': 10085, 'name': 'betrayal'}, {'id': 10594, 'name': 'money'}, {'id': 10726, 'name': 'gang'}, {'id': 15076, 'name': 'cat and mouse'}, {'id': 18023, 'name': 'criminal mastermind'}, {'id': 34117, 'name': 'cult film'}, {'id': 156121, 'name': 'ex-con'}, {'id': 159343, 'name': 'heist movie'}, {'id': 159434, 'name': 'one last job'}, {'id': 167104, 'name': 'loner'}, {'id': 192261, 'name': 'bank job'}, {'id': 207268, 'name': 'neo-noir'}, {'id': 208009, 'name': 'gun fight'}, {'id': 214983, 'name': 'crime epic'}]\""###load pakages##
pacman::p_load(pacman,GGally,ggthemes,ggplot2,ggvis,rio,shiny,rmarkdown,tidyr,stringr,lubridate,plotly,dplyr)
Ratings <- ("ratings.cvs")
Ratings_Small <- ("ratings_small.csv")
Links <- ("links.csv")
Links_Small <- ("links_small.csv")
Keywords <- ("keywords.csv")
##now this will produce a better looking data output##
head(Ratings)## [1] "ratings.cvs"head(Ratings_Small)## [1] "ratings_small.csv"head(Links_Small)## [1] "links_small.csv"###creating chunks of data ## and making the data less messy##
#Ratings_small
chunkSize <- 10000
con2 <- file(description = Ratings_Small, open = "r")
data2 <- read.table(con2, nrows = chunkSize ,header = T ,fill = T,sep = ",")
close(con2)
#Links
chunkSize <- 10000
con3 <- file(description = Links,open = "r")
data3 <- read.table(con3,nrows = chunkSize,header = T,fill = T,sep = ",")
close(con3)
#Links_Small
chunkSize <- 10000
con4 <- file(description = Links_Small,open = "r")
data4 <- read.table(con4, nrows = chunkSize,header = T,fill = T,sep = ",")
close(con4)
#Keywords
chunkSize <- 10000
con5 <- file(description = Keywords,open = "r")
data5 <- read.table(con5, nrows = chunkSize ,header = T, fill = T,sep = ",")
close(con5)
###now we have loaded data that would other wise freeze the pc##
#we have 4 chunks of data sets form four data diff data sets
#converting to factors#
data2$movieId <- as.factor(data2$movieId)
data3$imdbId <- as.factor(data3$imdbId)
data4$imdbId <- as.factor(data4$imdbId)
data3$movieId <- as.factor(data3$movieId)
data4$movieId <- as.factor(data4$movieId)
data2_data3 <- cbind(data3,data2[ ,3]) ##bnding the data by columns, adding info from only coloumn 3
# of the dataset data2 into the data set data3
colnames(data2_data3)[4] <- "ratings" #changing the coloumn name to our liking
X <- cbind(data2_data3,data2[ ,4]) #cbinding/ adding the 4 coloumn of data2 into the new data2_data3 and assigning it to X##
colnames(X)[5] <- "timestamp"
par(mfrow= c(2,1))
Movies_Data <- merge(X,data4, key= movieId) #merges the data together##
hist(Movies_Data$rating,col = "red",xlab = "Ratings",main = "HIST of Ratings")
rug(Movies_Data$ratings, lwd = 2,col = "grey") # adds grey lines to pin point the data points on the graph#
hist(Movies_Data$timestamp, col="blue",xlab = "movies id",main = "HIST of MOVIES id", breaks = 20)## Warning in breaks[-1L] + breaks[-nB]: NAs produced by integer overflow
summary(Movies_Data$userId)## Length Class Mode
## 0 NULL NULLsummary(Movies_Data$movieId)## 1 2 3 4 5 6 7 8 9 10
## 1 1 1 1 1 1 1 1 1 1
## 11 12 13 14 15 16 17 18 19 20
## 1 1 1 1 1 1 1 1 1 1
## 21 22 23 24 25 26 27 28 29 30
## 1 1 1 1 1 1 1 1 1 1
## 31 32 34 35 36 37 38 39 40 41
## 1 1 1 1 1 1 1 1 1 1
## 42 43 44 45 46 47 48 49 50 52
## 1 1 1 1 1 1 1 1 1 1
## 53 54 55 57 58 59 60 61 62 63
## 1 1 1 1 1 1 1 1 1 1
## 64 65 66 68 69 70 71 72 73 74
## 1 1 1 1 1 1 1 1 1 1
## 76 77 78 79 80 81 82 83 84 85
## 1 1 1 1 1 1 1 1 1 1
## 86 87 88 89 92 93 94 95 96 97
## 1 1 1 1 1 1 1 1 1 1
## 98 99 100 101 102 103 104 105 107 (Other)
## 1 1 1 1 1 1 1 1 1 6016library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha#now we have a one data frame from 3 data sets##
#NOW TO ALLOW THE CHART TO PLOT 2 CHARTS ON THE SAME PAGE##
describe(Movies_Data$ratings)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6115 3.43 1.15 3.5 3.52 0.74 0.5 5 4.5 -0.63 -0.23 0.01describe(Movies_Data)## vars n mean sd median trimmed
## movieId* 1 6115 4.104530e+03 2676.48 3734.0 3.970730e+03
## imdbId* 2 6115 5.035570e+03 2774.98 5111.0 5.042800e+03
## tmdbId 3 6108 2.034835e+04 25397.83 12138.5 1.615840e+04
## ratings 4 6115 3.430000e+00 1.15 3.5 3.520000e+00
## timestamp 5 6115 1.107375e+09 186591642.67 1108134263.0 1.097109e+09
## mad min max range skew kurtosis
## movieId* 3131.25 1.0 10000 9999.0 0.37 -0.92
## imdbId* 3384.78 1.0 10000 9999.0 -0.04 -1.10
## tmdbId 13492.40 2.0 416437 416435.0 4.88 42.46
## ratings 0.74 0.5 5 4.5 -0.63 -0.23
## timestamp 226281602.61 832228796.0 1475557281 643328485.0 0.39 -0.87
## se
## movieId* 34.23
## imdbId* 35.49
## tmdbId 324.97
## ratings 0.01
## timestamp 2386129.21