First upload

.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

Project/.gitignore

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+*.csv

Project/Movie Ratings.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes

Project/datasets/groupLens/source.txt

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+GroupLens Dataset ± 22.000.000 ratings
+
+link:
+http://files.grouplens.org/datasets/movielens/ml-latest.zip
+

Project/datasets/imdb/source.txt

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+IMDB dataset ± 60.000 ratings
+
+link:
+https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/movies.csv

Project/datasets/netflix/source.txt

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+Netflix dataset ±100.000.000 ratings (Yep that’s correct 100 million)
+
+link:
+https://www.igvita.com/downloads/Netflix-igvita.zip

Project/main.R

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+# (c) Deben Oldert
+# Checks the difference between multiple movie rating from different websites
+
+# Load libraries
+suppressMessages(library(dplyr))
+suppressMessages(library(stringr))
+
+# Set global vars
+wd <- getwd()
+
+# Set common functions
+
+returnPath <- function(pth) {
+  return(paste(wd, pth, sep = "/"))
+}
+
+# 1. IMDB
+# 2. GroupLens
+# 3. Netflix
+
+set_names <- c("IMDB", "GroupLens", "Netflix")
+
+# Loading .CSV's
+print("Prepairing IMDB...")
+imdb <- read.csv(returnPath("datasets/imdb/imdb.csv"), row.names=1)
+imdb <- select(tbl_df(imdb), title, rating, year)
+imdb <- mutate(imdb, title = paste(title, " (", year, ")", sep = ""))
+print("DONE")
+
+print("Prepairing GroupLens...")
+groupLens_movie <- read.csv(returnPath("datasets/groupLens/movies_frmt.csv"))
+groupLens_movie <- select(tbl_df(groupLens_movie), movieId, title, year)
+# Extract the year
+#groupLens_movie <- mutate(groupLens_movie, year = as.integer(str_match(title, "([0-9]{4})")[,1]))
+
+groupLens_rating <- read.csv(returnPath("datasets/groupLens/ratings.csv"))
+groupLens_rating <- tbl_df(groupLens_rating)
+groupLens_rating <- group_by(groupLens_rating, movieId)
+groupLens_rating <- summarise(groupLens_rating, rating = mean(rating, na.rm = TRUE))
+
+groupLens <- merge(groupLens_movie, groupLens_rating, by = intersect(names(groupLens_movie), names(groupLens_rating)), all = TRUE)
+
+# Cleanup
+remove(groupLens_rating)
+remove(groupLens_movie)
+
+print("DONE")
+
+print("Prepairing Netflix...")
+# Gonna be a clusterfuck w/ 2GB data (±100 million reviews)
+netflix_movie <- read.csv(returnPath("datasets/netflix/movie_titles.csv"))
+netflix_movie <- select(tbl_df(netflix_movie), movieId, title, year)
+netflix_movie <- mutate(netflix_movie, title_frmt = paste(title, " (", year, ")", sep = ""))
+
+# Create new and empty dataframe for final results
+netflix <- data.frame(title_frmt = character(0), year = integer(0), rating = numeric(0))
+
+# We need to loop through every movieId to find its .csv file
+# Then we calculate the average rating for the movie and store it in a new data.frame
+for (i in 1:nrow(netflix_movie)) {
+  row <- netflix_movie[i,]
+
+  print(i)
+
+  # Format the file name
+  # E.G:
+  # mv_0000001.csv
+  # mv_0027640.csv
+  csv_ <- "mv_"
+  for(j in nchar(row$movieId):6){
+    csv_ <- paste(csv_, "0", sep = "")
+  }
+  csv_ <- paste(csv_, row$movieId, ".csv", sep = "")
+
+  # Prepend the filesystem location (working directory)
+  csv_ <- paste(wd, "datasets/netflix", csv_, sep = "/")
+
+  netflix_rating <- read.csv(csv_)
+
+  if(is.null(netflix_rating)) { # If csv is empty => skip it
+    print(paste("Empty:", csv_))
+    next
+  }
+
+  netflix_rating <- tbl_df(netflix_rating)
+  netflix_rating <- summarise(netflix_rating, ratings = mean(rating, na.rm = TRUE))
+
+  # Append result to the netflix table
+  netflix <- bind_rows(netflix, data.frame(title_frmt = as.character(row$title_frmt), year = as.integer(as.character(row$year)), rating = netflix_rating$ratings))
+
+  # Cleanup
+  remove(csv_)
+  remove(netflix_rating)
+  remove(j)
+}
+
+# Cleanup
+remove(netflix_movie)
+remove(row)
+remove(i)
+remove(wd)
+
+print("DONE")
+
+print("Done Loading")
+
+print("Working on question no. 1...")
+
+# Define ranges
+y = 10
+
+x_min <- min(min(imdb$year, na.rm = TRUE), min(groupLens$year, na.rm = TRUE), min(netflix$year, na.rm = TRUE))
+x_max <- max(max(imdb$year, na.rm = TRUE), max(groupLens$year, na.rm = TRUE), max(netflix$year, na.rm = TRUE))
+# Define colors
+
+color <- c("blue", "red", "green")
+
+imdb_year_avg <- imdb %>%
+                group_by(year) %>%
+                summarise(rating = mean(rating, na.rm = TRUE))
+
+groupLens_year_avg <- groupLens %>%
+  group_by(year) %>%
+  summarise(rating = mean(rating, na.rm = TRUE) * 2)
+
+netflix_year_avg <- netflix %>%
+  group_by(year) %>%
+  summarise(rating = mean(rating, na.rm = TRUE) * 2)
+
+plot(imdb_year_avg,
+     type = "l",
+     ylim = c(0, 10),
+     col = color[1],
+     axes = F,
+     ann = T,
+     xlab = "Years",
+     ylab = "Avg. rating",
+     cex.lab=0.8, lwd=2)
+
+#text(axTicks(1), par("usr")[3] - 2, srt=45, adj = 1, labels = x_min:x_max, xpd=T, cex=0.8)
+
+box()
+
+lines(groupLens_year_avg, type = "l", lty = 2, lwd = 2, col = color[2])
+lines(netflix_year_avg, type = "l", lty = 3, lwd = 2, col = color[3])
+
+legend("topleft", set_names, cex = 0.8, col = color, lty=1:3, lwd = 2, bty="n")
+
+
+