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process_categories.R
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237 lines (177 loc) · 8.83 KB
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# This script aggregates information about review stars, dates, and votes by category.
# It could take an hour or more to run.
require(magrittr)
require(plyr)
# Get data from JSON
businesses <- jsonlite::stream_in(file("yelp_dataset_challenge_academic_dataset//yelp_academic_dataset_business.json"))
reviews <- jsonlite::stream_in(file("yelp_dataset_challenge_academic_dataset//yelp_academic_dataset_review.json"))
# Associate reviews and businesses with categories.
reviews <- join(reviews, businesses[,c("business_id","categories")], by="business_id",match="first")
# We're going to create two dataframes: reviews.long and businesses.long. In both of these, each row is a review/category pair
# or a business/category pair, respectively.
# First, name columns and allocate memory space for the large matrices
category.size <- reviews$categories %>% unlist %>% length
reviews.long <- cbind(
category = rep(NA, times = category.size),
business_id = rep(NA, times = category.size),
stars = rep(NA, times = category.size),
date = rep(NA, times = category.size),
votes.funny = rep(NA, times = category.size),
votes.useful = rep(NA, times = category.size),
votes.cool = rep(NA, times = category.size)
) %>% as.matrix
businesses.size <- businesses$categories %>% unlist %>% length
businesses.long <- cbind(
category = rep(NA, times = businesses.size),
business_id = rep(NA, times = businesses.size),
stars = rep(NA, times = businesses.size)
) %>% as.matrix
# Create a dataframe where each row is a review/category pair
j <- 1
for (i in 1:nrow(businesses)) {
sapply(unlist(businesses$categories[i]), function (x) {
businesses.long[j,1] <<- x
businesses.long[j,2] <<- businesses$business_id[i]
businesses.long[j,3] <<- businesses$stars[i]
j <<- j + 1
})
}
# Create a dataframe where each row is a business/category pair
j <- 1
for (i in 1:nrow(businesses)) {
sapply(unlist(businesses$categories[i]), function (x) {
reviews.long[j,1] <<- x
reviews.long[j,2] <<- reviews$business_id[i]
reviews.long[j,3] <<- reviews$stars[i]
reviews.long[j,4] <<- reviews$date[i]
reviews.long[j,5] <<- reviews$votes$funny[i]
reviews.long[j,6] <<- reviews$votes$useful[i]
reviews.long[j,7] <<- reviews$votes$cool[i]
j <<- j + 1
})
}
# Convert to dataframe
reviews.long <- data.frame(reviews.long)
businesses.long <- data.frame(businesses.long)
# Create a dataframe with categories
categories <- businesses$categories %>%
unlist %>%
table %>%
as.data.frame %>%
setNames(c("category","business.count"))
# Remove the categories with fewer than 10 businesses
categories <- categories[categories$business.count >= 10, ]
categories$category <- categories$category %>% as.character
# Calculate star distribution by categories
categories$stars.1 <- sapply(categories$category, function (x) {
reviews.long$stars[reviews.long$category==x & reviews.long$stars==1] %>% length
})
categories$stars.2 <- sapply(categories$category, function (x) {
reviews.long$stars[reviews.long$category==x & reviews.long$stars==2] %>% length
})
categories$stars.3 <- sapply(categories$category, function (x) {
reviews.long$stars[reviews.long$category==x & reviews.long$stars==3] %>% length
})
categories$stars.4 <- sapply(categories$category, function (x) {
reviews.long$stars[reviews.long$category==x & reviews.long$stars==4] %>% length
})
categories$stars.5 <- sapply(categories$category, function (x) {
reviews.long$stars[reviews.long$category==x & reviews.long$stars==5] %>% length
})
categories$businesses.1.0stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==1] %>% length
})
categories$businesses.1.5stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==1.5] %>% length
})
categories$businesses.2.0stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==2] %>% length
})
categories$businesses.2.5stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==2.5] %>% length
})
categories$businesses.3.0stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==3] %>% length
})
categories$businesses.3.5stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==3.5] %>% length
})
categories$businesses.4.0stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==4] %>% length
})
categories$businesses.4.5stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==4.5] %>% length
})
categories$businesses.5.0stars <- sapply(categories$category, function (x) {
businesses.long$stars[businesses.long$category==x & businesses.long$stars==5] %>% length
})
categories$mean.stars.reviews <- (categories$stars.1 + categories$stars.2*2 + categories$stars.3*3 +
categories$stars.4*4 + categories$stars.5*5) /
(categories$stars.1+categories$stars.2+categories$stars.3+categories$stars.4+categories$stars.5)
categories$mean.stars.businesses <- (categories$businesses.1.0stars +
categories$businesses.1.5stars * 1.5 +
categories$businesses.2.0stars * 2 +
categories$businesses.2.5stars * 2.5 +
categories$businesses.3.0stars * 3.0 +
categories$businesses.3.5stars * 3.5 +
categories$businesses.4.0stars * 4 +
categories$businesses.4.5stars * 4.5 +
categories$businesses.5stars * 5) /
(categories$business.count)
categories$review.count <- categories$stars.1 + categories$stars.2 + categories$stars.3 + categories$stars.4 + categories$stars.5
# Remove categories with few reviews
categories <- categories[categories$review.count>=10, ]
# Calculate percentages of reviews by star
categories$stars.1.percent <- (categories$stars.1 / categories$review.count *100) %>% round(2)
categories$stars.2.percent <- (categories$stars.2 / categories$review.count *100) %>% round(2)
categories$stars.3.percent <- (categories$stars.3 / categories$review.count *100) %>% round(2)
categories$stars.4.percent <- (categories$stars.4 / categories$review.count *100) %>% round(2)
categories$stars.5.percent <- (categories$stars.5 / categories$review.count * 100) %>% round(2)
# Sum up review attributes
categories$review.votes.funny <- sapply(categories$category, function (x) {
reviews.long$votes.funny[reviews.long$category==x] %>% as.character %>% as.numeric %>% sum
})
categories$review.votes.funny.ratio <- categories$review.votes.funny / categories$review.count
categories$review.votes.useful <- sapply(categories$category, function (x) {
reviews.long$votes.useful[reviews.long$category==x] %>% as.character %>% as.numeric %>% sum
})
categories$review.votes.useful.ratio <- categories$review.votes.useful / categories$review.count
categories$review.votes.cool <- sapply(categories$category, function (x) {
reviews.long$votes.cool[reviews.long$category==x] %>% as.character %>% as.numeric %>% sum
})
categories$review.votes.cool.ratio <- categories$review.votes.cool / categories$review.count
# Create a dataframe showing relationships between categories. Let's say there's a business belonging
# to three categories: Restaurants, Fast Food, and Pizza. We will add 3 rows to reflect the 3 associations:
# Restaurants<->Fast Food, Restaurants <-> Pizza, and Pizza <-> Fast Food. Then, we will count up the number
# of unique associations.
category.association <- sapply(businesses$categories, function (x) {
if (length(x) > 1) {
combn (x, 2) %>% t
}
})
category.association <- do.call("rbind", category.association) %>%
apply(MARGIN = 1, FUN = sort) %>%
t %>%
as.data.frame %>%
plyr::count(c("V1", "V2")) %>%
setNames(c("category1","category2","count"))
# Create an abbreviated version of the categories dataframe
categories.app <- categories[ , c(
"category",
"business.count",
"review.count",
"mean.stars.businesses",
"businesses.1.0stars",
"businesses.1.5stars",
"businesses.2.0stars",
"businesses.2.5stars",
"businesses.3.0stars",
"businesses.3.5stars",
"businesses.4.0stars",
"businesses.4.5stars",
"businesses.5.0stars"
)]
category.association.app <- category.association[category.association$count >=5, ]
save.image("categories.Rdata")
save("categories.app", "category.association.app", file = "categories.app.Rdata")
save("reviews.long", file = "reviews.long.Rdata")