AS12 YelpDemo - 資料分析示範
Group 4 B034020012謝雨靜 B034020027陳韻卉 B044012015王譯苓 M064111025 黃威豪 M064111039王 欣 行傳所 孟祥瑄
2018-08-31 15:34:35
rm(list=ls(all=T)); gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 516963 27.7 1172462 62.7 609148 32.6
## Vcells 1005352 7.7 8388608 64.0 1604909 12.3library(knitr); opts_chunk$set(comment = NA)
library(Matrix)
library(dplyr)
#install.packages("googleVis")
library(googleVis)
library(doParallel)
library(Rtsne)
#install.packages("wordcloud")
library(wordcloud)
library(RColorBrewer)
library(randomcoloR)
#install.packages("d3heatmap")
library(d3heatmap)
library(morpheus)
library(FactoMineR)
library(factoextra)
library(highcharter)
library(tm)
library(slam)
library(MASS)
options(width=100, gvis.plot.tag='chart', digits=4, scipen=60)
#options(width=100, digits=4, scipen=60)Load Data
prep2 = "prep2/"
load(paste0(prep2,"businesses.rdata"))
load(paste0(prep2,"reviews.rdata"))Merge sentiment score into rev
rev = cbind(rev, senti[,-1])
range(rev$date)[1] "2004-07-22" "2017-07-26"#rev$year = as.integer(format(rev$date, "%Y"))
breaks = as.Date(c("2004-07-22", paste0(2005:2017, "-07-27")))
rev$year = as.integer(cut(rev$date, breaks)) + 2004(1) 評論數量最多的100個商店
1.1 Top100 bid
b100 = rev %>% count(bid) %>% arrange(desc(n)) %>% head(100) %>% .$bid1.2 Summarise reviews by bid and year
df = rev %>% filter(bid %in% b100) %>%
group_by(bid, year) %>%
summarise(
positive = mean(positive),
negative = mean(negative),
sad = mean(sadness),
n = n()
)1.3 Biz that have review every year after 2009
y09 = df %>% filter(year >= 2009) %>% count(bid) %>% filter(nn == 9) %>% .$bid1.4 Google Motion Chart
df %>% filter(year >= 2010 & year <= 2016 & bid %in% y09) %>%
data.frame %>%
gvisMotionChart("bid", "year") %>% plot<!-- MotionChart generated in R 3.5.1 by googleVis 0.6.2 package -->
<!-- Fri Aug 31 15:34:43 2018 -->
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data.addColumn('string','bid');
data.addColumn('number','year');
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</div>(2) 商業類別的動態比較
2.1 依商業類別彙總評論資料
load(paste0(prep2, "cat822.rdata"))選定商業類別
select1 = c("Fitness & Instruction","fitness","Aerial Fitness","Barre Classes","Boot Camps","Boxing","Cardio Classes","Dance Studios","Golf Lessons","Gyms","Circuit Training Gyms","Interval Training Gyms","Martial Arts","Brazilian Jiu-jitsu","Chinese Martial Arts","Karate","Kickboxing","Muay Thai","Taekwondo","Meditation Centers","Pilates" ,"Qi Gong" ,"Self-defense Classes" , "Swimming Lessons/Schools" , "Tai Chi","Trainers","Yoga")
select2 = select1[select1 %in% X@Dimnames[[2]]]整理出屬於fitness & intruction類別(包含底下子類別)的所有商店
select1 %in% X@Dimnames[[2]] [1] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE
[17] TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUEaa = B$bid[(rowSums(X[,select2]) > 0)]
Fitness <- subset(B,bid %in% aa)
Fitness <- merge(Fitness,bizatt,by = "bid")
head(Fitness) bid address business_id city is_open latitude
1 21 7060 W Warm Springs Rd, Ste 170 C8-Y-b6GA11afcixfC9Dwg Las Vegas TRUE 36.06
2 34 14415 N Scottsdale Rd, Ste 600 WjYI4EbY_mshEKPMJg7jpA Scottsdale TRUE 33.61
3 149 1011 N Gammon Rd fvtpeQt4Oj3a5snE2UDF4g Middleton TRUE 43.08
4 233 555 College Dr, Ste B vhgdZJLMR1a_4zkAk1GD2Q Henderson TRUE 36.01
5 364 7750 E Redfield Rd, Ste D102 2vOh_VdWErcmuPJNHCHxoQ Scottsdale TRUE 33.61
6 395 2790 W Horizon Ridge Pkwy, Ste 120 qvheARnCPSrb_UaAELV4jw Henderson TRUE 36.00
longitude name neighborhood postal_code review_count stars state
1 -115.2 Crossfit Culmination South Southwest 89113 13 5.0 NV
2 -111.9 Pilates Joe <NA> 85260 5 4.5 AZ
3 -89.5 Anytime Fitness <NA> 53562 5 4.0 WI
4 -115.0 Cox and Gallacher Martial Arts <NA> 89015 4 5.0 NV
5 -111.9 Reality Defense Training <NA> 85260 7 5.0 AZ
6 -115.1 Bodies by Pilates Anthem 89052 5 5.0 NV
AcceptsInsurance AgesAllowed Alcohol BYOB BYOBCorkage BikeParking BusinessAcceptsBitcoin
1 NA <NA> <NA> NA <NA> TRUE FALSE
2 NA <NA> <NA> NA <NA> TRUE NA
3 NA <NA> <NA> NA <NA> NA NA
4 NA <NA> <NA> NA <NA> NA NA
5 NA <NA> <NA> NA <NA> NA NA
6 NA <NA> <NA> NA <NA> NA NA
BusinessAcceptsCreditCards ByAppointmentOnly Caters CoatCheck Corkage DogsAllowed DriveThru
1 TRUE FALSE NA NA NA FALSE NA
2 TRUE TRUE NA NA NA NA NA
3 NA NA NA NA NA NA NA
4 TRUE NA NA NA NA NA NA
5 TRUE NA NA NA NA NA NA
6 TRUE TRUE NA NA NA NA NA
GoodForDancing GoodForKids HappyHour HasTV NoiseLevel Open24Hours OutdoorSeating
1 NA TRUE NA NA <NA> NA NA
2 NA FALSE NA NA <NA> NA NA
3 NA FALSE NA NA <NA> NA NA
4 NA TRUE NA NA <NA> NA NA
5 NA TRUE NA NA <NA> NA NA
6 NA FALSE NA NA <NA> NA NA
RestaurantsAttire RestaurantsCounterService RestaurantsDelivery RestaurantsGoodForGroups
1 <NA> NA NA NA
2 <NA> NA NA NA
3 <NA> NA NA NA
4 <NA> NA NA NA
5 <NA> NA NA NA
6 <NA> NA NA NA
RestaurantsPriceRange2 RestaurantsReservations RestaurantsTableService RestaurantsTakeOut Smoking
1 1 NA NA NA <NA>
2 NA NA NA NA <NA>
3 NA NA NA NA <NA>
4 NA NA NA NA <NA>
5 NA NA NA NA <NA>
6 NA NA NA NA <NA>
WheelchairAccessible WiFi casual classy divey hipster intimate romantic touristy trendy upscale
1 TRUE <NA> NA NA NA NA NA NA NA NA NA
2 FALSE <NA> NA NA NA NA NA NA NA NA NA
3 NA <NA> NA NA NA NA NA NA NA NA NA
4 NA <NA> NA NA NA NA NA NA NA NA NA
5 NA <NA> NA NA NA NA NA NA NA NA NA
6 NA <NA> NA NA NA NA NA NA NA NA NA
friday monday saturday sunday thursday tuesday wednesday garage lot street valet validated
1 NA NA NA NA NA NA NA FALSE TRUE FALSE FALSE FALSE
2 NA NA NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA FALSE FALSE FALSE FALSE FALSE
5 NA NA NA NA NA NA NA FALSE FALSE FALSE FALSE FALSE
6 NA NA NA NA NA NA NA NA NA NA NA NA
dairy-free gluten-free halal kosher soy-free vegan vegetarian breakfast brunch dessert dinner
1 NA NA NA NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA NA NA NA
latenight lunch africanamerican asian coloring curly extensions kids perms straightperms
1 NA NA NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA NA NA
background_music dj jukebox karaoke live no_music video
1 NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NAfitness & intruction商業類別的探索
#先刪除太多NA的欄位
Fitness <- Fitness[ lapply( Fitness, function(x) (sum(is.na(x)) / length(x) )) < 0.2 ]
#發現刪除了來自B dataframe的neighbor，bizatt只有剩下GoodforKids
summary(Fitness) bid address business_id city is_open
Min. : 21 Length:3615 Length:3615 Length:3615 Mode :logical
1st Qu.: 39057 Class :character Class :character Class :character FALSE:576
Median : 79093 Mode :character Mode :character Mode :character TRUE :3039
Mean : 78687
3rd Qu.:117394
Max. :156619
latitude longitude name postal_code review_count
Min. :33.2 Min. :-115.42 Length:3615 Length:3615 Min. : 3.0
1st Qu.:33.6 1st Qu.:-112.13 Class :character Class :character 1st Qu.: 4.0
Median :36.1 Median :-111.84 Mode :character Mode :character Median : 7.0
Mean :37.6 Mean : -97.05 Mean : 13.8
3rd Qu.:41.5 3rd Qu.: -80.06 3rd Qu.: 15.0
Max. :56.0 Max. : 9.28 Max. :444.0
stars state GoodForKids
Min. :1.0 Length:3615 Mode :logical
1st Qu.:3.5 Class :character FALSE:1263
Median :4.5 Mode :character TRUE :1807
Mean :4.2 NA's :545
3rd Qu.:5.0
Max. :5.0 2.2 比較討論聲量相似的商業類別
select1 = c("Aerial Fitness","Barre Classes","Boot Camps","Boxing","Cardio Classes","Dance Studios","Golf Lessons","Gyms","Circuit Training Gyms","Interval Training Gyms","Martial Arts","Brazilian Jiu-jitsu","Chinese Martial Arts","Karate","Kickboxing","Muay Thai","Taekwondo","Meditation Centers","Pilates" ,"Qi Gong" ,"Self-defense Classes" , "Swimming Lessons/Schools" , "Tai Chi","Trainers","Yoga")
df = cat822 %>%
filter(year >= 2010) %>%
filter(category %in% select1) %>%
dplyr::select(category, year, negative, stars, useful, n,
positive, joy, anticipation, trust, surprise,
sadness, anger, disgust, fear, cool, funny
) %>%
mutate_at(c(3:5,7:17), scale) %>% # 除了數量，其餘皆做標準化
data.frame
p = gvisMotionChart(df, "category", "year")
plot(p)<!-- MotionChart generated in R 3.5.1 by googleVis 0.6.2 package -->
<!-- Fri Aug 31 15:34:43 2018 -->
<!-- jsHeader -->
<script type="text/javascript">
// jsData
function gvisDataMotionChartID1d6c3f8d7a5b () {
var data = new google.visualization.DataTable();
var datajson =
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[
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[
"Meditation Centers",
2016,
-0.5157726028,
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425,
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[
"Meditation Centers",
2017,
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[
"Karate",
2010,
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87,
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[
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2011,
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[
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2012,
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[
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2013,
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[
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2014,
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[
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2015,
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[
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2016,
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[
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2017,
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[
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2010,
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0.7566809634,
1.298041837,
19,
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2011,
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2012,
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2013,
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2014,
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2015,
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2016,
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2017,
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[
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2010,
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2011,
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2012,
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2013,
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2014,
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2015,
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[
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2016,
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2017,
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[
"Kickboxing",
2010,
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0.8284065752,
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11,
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[
"Kickboxing",
2011,
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27,
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5.095181373,
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[
"Kickboxing",
2012,
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52,
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[
"Kickboxing",
2013,
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69,
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[
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2014,
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[
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2015,
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[
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2016,
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2017,
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[
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2010,
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7,
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2011,
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34,
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2012,
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2013,
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2014,
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2015,
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[
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2016,
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[
"Tai Chi",
2017,
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];
data.addColumn('string','category');
data.addColumn('number','year');
data.addColumn('number','negative');
data.addColumn('number','stars');
data.addColumn('number','useful');
data.addColumn('number','n');
data.addColumn('number','positive');
data.addColumn('number','joy');
data.addColumn('number','anticipation');
data.addColumn('number','trust');
data.addColumn('number','surprise');
data.addColumn('number','sadness');
data.addColumn('number','anger');
data.addColumn('number','disgust');
data.addColumn('number','fear');
data.addColumn('number','cool');
data.addColumn('number','funny');
data.addRows(datajson);
return(data);
}
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style="width: 600; height: 500;">
</div>(3) 四個運動項目 商業類別
3.1 設定商店評論選擇條件
#選擇boxing在2011年以後每年都有評論
#商業類別包含boxing
Boxing = rev %>%
filter(bid %in% B$bid[X[,"Boxing"]]) %>%
filter(year >= 2011) %>%
group_by(bid) %>%
summarise(
n_year = n_distinct(year), #
n_rev = n() #
) %>%
filter(
n_year == 7,
n_rev > 50
) %>%
dplyr::select(bid) %>%
left_join(B)Joining, by = "bid"nrow(Boxing)[1] 7#選擇Karate在2011年以後每年都有評論
#商業類別包含Karate
Karate = rev %>%
filter(bid %in% B$bid[X[,"Karate"]]) %>%
filter(year >= 2011) %>%
group_by(bid) %>%
summarise(
n_year = n_distinct(year), #
n_rev = n() #
) %>%
filter(
n_year == 7
) %>%
dplyr::select(bid) %>%
left_join(B)Joining, by = "bid"nrow(Karate)[1] 5#選擇Kickboxing在2011年後每年都有評論
#商業類別包含Kickboxing
Kickboxing = rev %>%
filter(bid %in% B$bid[X[,"Karate"]]) %>%
filter(year >= 2011) %>%
group_by(bid) %>%
summarise(
n_year = n_distinct(year), #
n_rev = n() #
) %>%
filter(
n_year == 7
) %>%
dplyr::select(bid) %>%
left_join(B)Joining, by = "bid"nrow(Kickboxing)[1] 5#選擇Taichi在2011年後至少6年有評論
#商業類別包含TaiChi
TaiChi = rev %>%
filter(bid %in% B$bid[X[,"Tai Chi"]]) %>%
filter(year >= 2011) %>%
group_by(bid) %>%
summarise(
n_year = n_distinct(year), #
n_rev = n() #
) %>%
filter(
n_year >=6
) %>%
dplyr::select(bid) %>%
left_join(B)Joining, by = "bid"nrow(TaiChi)[1] 53.2 選出評論
選出這一些商店在2011年之後的所有評論，放在資料框R裡面
R = rev %>% filter(bid %in% Boxing$bid & year >= 2011)
par(mar=c(3,4,3,2), cex=0.8)
R %>% count(bid) %>% .$n %>% hist(8, main="No. Reviews per Biz")
R = rev %>% filter(bid %in% Karate$bid & year >= 2011)
par(mar=c(3,4,3,2), cex=0.8)
R %>% count(bid) %>% .$n %>% hist(8, main="No. Reviews per Biz")
R = rev %>% filter(bid %in% Kickboxing$bid & year >= 2011)
par(mar=c(3,4,3,2), cex=0.8)
R %>% count(bid) %>% .$n %>% hist(8, main="No. Reviews per Biz")
R = rev %>% filter(bid %in% TaiChi$bid & year >= 2011)
par(mar=c(3,4,3,2), cex=0.8)
R %>% count(bid) %>% .$n %>% hist(8, main="No. Reviews per Biz")
(4) 從平台取出評論內容：txt
#########################################################
# library(dplyr)
# library(sparklyr)
# Sys.setenv(SPARK_HOME="/usr/local/spark/spark-2.1.0-bin-hadoop2.7/")
# config <- spark_config()
# config$spark.ui.port = "4044"
# config$spark.executor.memory = "4G"
# config$spark.driver.memory = "4G"
# config$spark.yarn.executor.memoryOverhead = "4096"
# sc <- spark_connect(master = "spark://hnamenode:7077", config = config)
#
# yelp = "hdfs://192.168.1.100:9000/home/tonychuo/yelp10/pq01/"
# Text = spark_read_parquet(sc, "Text", paste0(yelp, "Text"))
# txt = Text %>% filter(rid %in% R$rid) %>% collect
#
# spark_disconnect(sc)# nrow(txt)總共有10,925篇評論
# c(setequal(R$rid, txt$rid), identical(R$rid, txt$rid))從平台取出的評論次序和R$rid是不同的，所以需要排序一下
# txt = left_join(R[,"rid",F], txt)
# identical(R$rid, txt$rid)從主機取出話題評分
ypath = "./prep2/"
load(paste0(ypath, "empath.rdata"))
empath = left_join(R[,"rid",F], empath)Joining, by = "rid"identical(R$rid, empath$rid)[1] TRUEgc() # release memory used (Mb) gc trigger (Mb) max used (Mb)
Ncells 2584954 138.1 5298101 283 5298101 283
Vcells 101458218 774.1 883446719 6740 1025279517 7822par(mar=c(3,4,3,2), cex=0.8)
R$year %>% table %>% barplot(main="No. Review by Year")
(5) 聲量、星等、互動、情緒
5.1 商店間的動態比較
df = R %>% group_by(bid, year) %>%
mutate(engage = useful + cool + funny) %>%
summarise_at(
c("engage", "stars", "positive", "negative"),
mean) %>%
left_join(count(R, bid, year)) %>%
data.frameJoining, by = c("bid", "year")p = gvisMotionChart(df, "bid", "year")
plot(p)<!-- MotionChart generated in R 3.5.1 by googleVis 0.6.2 package -->
<!-- Fri Aug 31 15:35:09 2018 -->
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</div>5.2 加入店名、去除極端值
df = left_join(df, B[,c("bid","name")]) # merge in biz nameJoining, by = "bid"df = cbind(df[,-c(1:2)], df[,1:2,F]) %>% # move bid to the last column
data.frame
df$name <- paste0(df$bid,df$name) # 避免有相同店名卻不同店家的狀況產生
p = gvisMotionChart(df, "name", "year")
plot(p)<!-- MotionChart generated in R 3.5.1 by googleVis 0.6.2 package -->
<!-- Fri Aug 31 15:35:09 2018 -->
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data.addColumn('number','n');
data.addColumn('number','bid');
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</div>5.3 星等上升、下降最快的商店
使用線性回歸找出星等上升、下降最快的商店
library(stringr)
bx = sapply(split(df, df$bid), function(x) {
lm(stars ~ year, x) %>% coef %>% `[`("year") }) %>% # take lm coef.
.[ abs(.) > 0.15 ] %>% #
names %>% str_extract("[0-9]*") %>%
as.integerp = df %>% filter(bid %in% bx) %>% gvisMotionChart("name", "year")
plot(p)<!-- MotionChart generated in R 3.5.1 by googleVis 0.6.2 package -->
<!-- Fri Aug 31 15:35:09 2018 -->
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</div>(6) 商店與話題
6.1 商店話題矩陣與熱圖
Make biz-theme matrix - mx
library(d3heatmap)
mx = sapply(split(empath[,-1], R$bid), colMeans) %>% t # biz-theme matrixbnames = B$name[match(rownames(mx), B$bid)]
rownames(mx) = bnamesSelect themes with medium-high weights
rx = colSums(mx) %>% quantile(c(0.60, 0.95)) #
mx = mx[, colSums(mx) > rx[1] & colSums(mx) < rx[2]] #Check the range of weights
par(cex=0.8)
hist(log(mx+1e-4))
6.2 D3heatmap
mx %>% {log(.+1e-4)} %>% t %>% d3heatmap(color=rev(cm.colors(9)))6.3 熱圖 + 互動式集群分析
這個工具不能投射在網頁上，只能直接在Rstudio裡面做
library(morpheus)
mx %>% {log(.+1e-4)} %>% morpheus(7) 商店的情緒分佈
將商店投射到尺度縮減之後的情緒平面上
7.1 建立商店情緒矩陣
pcx = sapply(split(R[,10:19], R$bid), colMeans) %>% t
rownames(pcx) = bnames7.2 尺度縮減
library(FactoMineR)
library(factoextra)
pcx = pcx %>% scale %>% PCA(ncp=10, graph=F) # par(cex=0.5)
# barplot(pcx$eig[1:4,3],names=1:10,main="Accumulated Variance",
# xlab="No. Components", ylab="% of Variance")
# abline(h=seq(0,100,10),col='lightgray')前三個主成分已經涵蓋了90%的變異
7.3 情緒平面上的商店
source("bipcx.R")
N = n_distinct(R$bid)
bipcx(pcx,1,2,10,N,t1="Strength",t2="Valence",
obs='Business', main="Strength & Valence of Sentiment")bipcx(pcx,3,2,10,N,t1="Arosual",t2="Valence",
obs='Business', main="Strength & Valence of Sentiment")(8) 文字分析與字雲
8.1 建立字頻表 (文件字詞矩陣)
library(tm)
dtm = txt$text %>%
iconv(to = "utf-8", sub="") %>%
VectorSource %>% Corpus %>%
tm_map(content_transformer(tolower)) %>%
tm_map(removePunctuation) %>%
tm_map(removeWords, stopwords("english")) %>%
tm_map(stemDocument) %>%
DocumentTermMatrix %>%
removeSparseTerms(0.998)
dtm # (documents: 10925, terms: 2520)8.2 使用TF-IDF篩選字詞
library(slam)
tfidf = tapply(dtm$v/row_sums(dtm)[dtm$i], dtm$j, mean) *
log2(nrow(dtm)/col_sums(dtm > 0))
summary(tfidf)dtm = dtm[, tfidf > 0.1471 ]
dtm = dtm[,order(-col_sums(dtm))]
dim(dtm)8.3 使用tSNE做尺度縮減
library(Rtsne)
n = 800
tsne = dtm[, 1:n] %>% as.data.frame.matrix %>%
scale %>% t %>% Rtsne(
check_duplicates = FALSE, theta=0.0, max_iter=3200)8.4 層級式集群分析
Y = tsne$Y # tSNE coordinates
d = dist(Y) # distance matrix
hc = hclust(d) # hi-clustering
K = 100 # number of clusters
g = cutree(hc,K) # cut into K clusters
table(g) %>% as.vector %>% sort # sizes of clusters8.5 文字雲
library(randomcoloR)
library(wordcloud)
wc = col_sums(dtm[,1:n])
colors = distinctColorPalette(K)
png("fig/japs2.png", width=3200, height=1800)
textplot(
Y[,1], Y[,2], colnames(dtm)[1:n], show=F,
col=colors[g],
cex= 0.2 + 1.25 * sqrt(wc/mean(wc)),
font=2)
dev.off()(9) 商務屬性 Business Attributes
9.1 bizatt 資料框
summary(bizatt) bid AcceptsInsurance AgesAllowed Alcohol BYOB
Min. : 1 Mode :logical 18plus : 86 beer_and_wine: 6444 Mode :logical
1st Qu.: 39150 FALSE:2149 19plus : 63 full_bar : 18767 FALSE:857
Median : 78310 TRUE :6426 21plus : 211 none : 19022 TRUE :48
Mean : 78311 NA's :147684 allages: 35 NA's :112026 NA's :155354
3rd Qu.:117462 NA's :155864
Max. :156639
BYOBCorkage BikeParking BusinessAcceptsBitcoin BusinessAcceptsCreditCards
no : 752 Mode :logical Mode :logical Mode :logical
yes_corkage: 157 FALSE:16419 FALSE:8663 FALSE:7579
yes_free : 491 TRUE :57064 TRUE :211 TRUE :113145
NA's :154859 NA's :82776 NA's :147385 NA's :35535
ByAppointmentOnly Caters CoatCheck Corkage DogsAllowed DriveThru
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:18059 FALSE:17221 FALSE:6887 FALSE:512 FALSE:7642 FALSE:3686
TRUE :16714 TRUE :17306 TRUE :1089 TRUE :140 TRUE :3568 TRUE :2352
NA's :121486 NA's :121732 NA's :148283 NA's :155607 NA's :145049 NA's :150221
GoodForDancing GoodForKids HappyHour HasTV NoiseLevel Open24Hours
Mode :logical Mode :logical Mode :logical Mode :logical average : 26119 Mode :logical
FALSE:6470 FALSE:11114 FALSE:2478 FALSE:21116 loud : 3908 FALSE:318
TRUE :2062 TRUE :47232 TRUE :5937 TRUE :22601 quiet : 8947 TRUE :29
NA's :147727 NA's :97913 NA's :147844 NA's :112542 very_loud: 1670 NA's :155912
NA's :115615
OutdoorSeating RestaurantsAttire RestaurantsCounterService RestaurantsDelivery
Mode :logical casual: 43277 Mode :logical Mode :logical
FALSE:29680 dressy: 1397 FALSE:150 FALSE:35526
TRUE :20619 formal: 125 TRUE :246 TRUE :11264
NA's :105960 NA's :111460 NA's :155863 NA's :109469
RestaurantsGoodForGroups RestaurantsPriceRange2 RestaurantsReservations RestaurantsTableService
Mode :logical Min. :1 Mode :logical Mode :logical
FALSE:6223 1st Qu.:1 FALSE:28014 FALSE:14318
TRUE :43391 Median :2 TRUE :18496 TRUE :25636
NA's :106645 Mean :2 NA's :109749 NA's :116305
3rd Qu.:2
Max. :4
NA's :59929
RestaurantsTakeOut Smoking WheelchairAccessible WiFi casual
Mode :logical no : 3019 Mode :logical free: 20924 Mode :logical
FALSE:5355 outdoor: 3513 FALSE:5815 no : 21954 FALSE:23947
TRUE :49277 yes : 1140 TRUE :38202 paid: 543 TRUE :18942
NA's :101627 NA's :148587 NA's :112242 NA's:112838 NA's :113370
classy divey hipster intimate romantic touristy
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:41968 FALSE:26060 FALSE:41903 FALSE:42299 FALSE:42367 FALSE:42658
TRUE :921 TRUE :1111 TRUE :963 TRUE :590 TRUE :522 TRUE :231
NA's :113370 NA's :129088 NA's :113393 NA's :113370 NA's :113370 NA's :113370
trendy upscale friday monday saturday sunday
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:40921 FALSE:42492 FALSE:1857 FALSE:5698 FALSE:1726 FALSE:5113
TRUE :1968 TRUE :397 TRUE :4534 TRUE :693 TRUE :4665 TRUE :1278
NA's :113370 NA's :113370 NA's :149868 NA's :149868 NA's :149868 NA's :149868
thursday tuesday wednesday garage lot street
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:3841 FALSE:5738 FALSE:5247 FALSE:84470 FALSE:54451 FALSE:72745
TRUE :2550 TRUE :653 TRUE :1144 TRUE :4950 TRUE :34969 TRUE :16675
NA's :149868 NA's :149868 NA's :149868 NA's :66839 NA's :66839 NA's :66839
valet validated dairy-free gluten-free halal kosher
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:87761 FALSE:86762 FALSE:231 FALSE:225 FALSE:243 FALSE:245
TRUE :1659 TRUE :440 TRUE :18 TRUE :24 TRUE :6 TRUE :4
NA's :66839 NA's :69057 NA's :156010 NA's :156010 NA's :156010 NA's :156010
soy-free vegan vegetarian breakfast brunch dessert
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:243 FALSE:128 FALSE:177 FALSE:39214 FALSE:39388 FALSE:41418
TRUE :6 TRUE :121 TRUE :72 TRUE :3726 TRUE :3552 TRUE :1522
NA's :156010 NA's :156010 NA's :156010 NA's :113319 NA's :113319 NA's :113319
dinner latenight lunch africanamerican asian coloring
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:27638 FALSE:40462 FALSE:25109 FALSE:751 FALSE:598 FALSE:279
TRUE :15302 TRUE :2478 TRUE :17831 TRUE :423 TRUE :576 TRUE :1126
NA's :113319 NA's :113319 NA's :113319 NA's :155085 NA's :155085 NA's :154854
curly extensions kids perms straightperms background_music
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:399 FALSE:570 FALSE:693 FALSE:745 FALSE:672 FALSE:6193
TRUE :1006 TRUE :835 TRUE :712 TRUE :660 TRUE :502 TRUE :1833
NA's :154854 NA's :154854 NA's :154854 NA's :154854 NA's :155085 NA's :148233
dj jukebox karaoke live no_music video
Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:6312 FALSE:6895 FALSE:7785 FALSE:6621 FALSE:8026 FALSE:7819
TRUE :1714 TRUE :1131 TRUE :241 TRUE :1405 NA's :148233 TRUE :207
NA's :148233 NA's :148233 NA's :148233 NA's :148233 NA's :148233
bizatt$RestaurantsPriceRange2[ X[,"Restaurants"] ] %>% table(useNA="ifany").
1 2 3 4 <NA>
19013 24917 2845 537 4299 grep("mex|ita",colnames(X),T, value=T) [1] "Italian" "Mexican" "Tex-Mex"
[4] "Hospitals" "Vitamins & Supplements" "Rehabilitation Center"
[7] "Guitar Stores" "Meditation Centers" "Emergency Pet Hospital"
[10] "New Mexican Cuisine" italian = X[,"Italian"] & X[,"Restaurants"]
mexican = X[,"Mexican"] & X[,"Restaurants"]
c(sum(italian), sum(mexican), sum(italian & mexican))[1] 4410 3913 25bizatt$RestaurantsPriceRange2[ italian ] %>% table(useNA="ifany").
1 2 3 4 <NA>
788 2698 519 70 335 bizatt$RestaurantsPriceRange2[ mexican ] %>% table(useNA="ifany").
1 2 3 4 <NA>
2286 1388 41 5 193