Spotkanie Entuzjastów R, Politechnika Warszawska, 17.03.2016. Przykłady: wizualizacja danych, klasteryzacja użytkowników, wykrywanie anomalii, prognozowanie w Google Analytics i R.

19. 100 150 00:01:20
120 300 00:02:15

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30. require(RGoogleAnalytics)
client.id <- "xxxxxxxxxxxx.apps.googleusercontent.com"
client.secret <- "zzzzzzzzzzzz"
token <- Auth(client.id,client.secret)
# Save the token object for future sessions
save(token,file="./token_file")

32. # Get the Sessions by Month in 2014
query.list <- Init(start.date = "2014-01-01",
end.date = "2014-01-31",
dimensions = "ga:date",
metrics = "ga:sessions",
table.id = "ga:000000")
# Create the Query Builder object
ga.query <- QueryBuilder(query.list)
# Extract the data and store it in a data-frame
ga.data <- GetReportData(ga.query, token)

33. > head(ga.data)
date sessions
1 20140101 39
2 20140102 46
3 20140103 47
4 20140104 53
5 20140105 49
6 20140106 15

35. # Get the Sessions by dayOfWeek in 2013
query.list <- Init(start.date = "2013-01-01",
end.date = "2013-12-31",
dimensions = "ga:dayOfWeek,ga:date",
metrics = "ga:sessions",
table.id = "ga:000000")
# Processing Query to ga.data
...
ggplot(data = ga.data, aes(x = dayOfWeek, y = sessions)) +
geom_boxplot()

37. # Get the Sessions by date in 2013-2015
query.list <- Init(start.date = "2013-01-01",
end.date = "2015-12-31",
dimensions = "ga:date",
metrics = "ga:sessions",
table.id = "ga:000000")
# Processing Query to ga.data
...

38. # Plot for calendar
calendar_sessions <- ggplot(data, aes(monthweek, weekdayf,
fill = sessions)) +
geom_tile(colour = "white") +
facet_grid(year~monthf) +
scale_fill_gradient(high="#D61818",low="#B5E384") +
labs(title = "Kalendarz") +
xlab("Tydzien miesiaca") +
ylab("")
# View calendar
calendar_sessions

40. install.packages("devtools")
devtools::install_github("twitter/AnomalyDetection")
library(AnomalyDetection)
# Processing Query to ga.data
...
# Anomaly detection
res = AnomalyDetectionTs(ga.data, max_anoms=0.02,
direction='both', plot=TRUE)
res$plot

42. ...
library(forecast)
...
forecast <- forecast.HoltWinters(forecastmodel, h=26)
# 26 days in future
...

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51. <script>
dataLayer = [{
'level': 'advanced',
'fingerprint' : '123456'
}];
</script>
https://goo.gl/1X84fY

54. # Get the Sessions by Month in 2014
query.list <- Init(start.date = "2014-01-01",
end.date = "2014-12-31",
dimensions = "ga:dimension01,
ga:contentGroup01",
metrics = "ga:contentGroupUniqueViews01",
table.id = "ga:000000")
# Create the Query Builder object
ga.query <- QueryBuilder(query.list)
# Extract the data and store it in a data-frame
ga.data <- GetReportData(ga.query, token)

55. > head(ga.data)
Beginner Intermediate Advanced
191352 0 2 42
990977 0 4 32
770561 0 4 48
898022 0 5 21
277510 0 6 31
644227 0 6 44

56. # K-Means Cluster Analysis
fit <- kmeans(ga.data, 3) # 3 clusters
...
# Append cluster assignment
data.fit <- data.frame(ga.data, fit$cluster)
head(data.fit)

57. > data.fit
Beginner Intermediate Advanced fit.cluster
266876 9 45 4 1
965265 9 51 7 1
...
981924 19 10 8 2
732529 19 16 1 2
...
377795 2 7 38 3
918083 2 8 28 3

58. > fit$centers
Beginner Intermediate Advanced
1 7.011765 38.42353 5.023529 # level: intermediate
2 25.530435 10.06087 4.713043 # level: beginner
3 3.628571 5.90000 32.657143 # level: advanced

59. # 3d chart
install.packages("plotly")
library(plotly)
result <- data.frame(df, fit$cluster)
plot_ly(result, x = result$level1, y = result$level2, z =
result$level3, type = "scatter3d", mode = "markers",
color=factor(result$fit.cluster))