-
Notifications
You must be signed in to change notification settings - Fork 1
/
03_data_tidying.R
262 lines (193 loc) · 8.39 KB
/
03_data_tidying.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
# Daten von APIs abfragen ---------------------------------------------
# Packages installieren; nur einmal nötig
# install.packages(c("googleAuthR",
# "googleAnalyticsR",
# "searchConsoleR",
# "lubridate",
# "jsonlite"))
library(googleAuthR)
library(googleAnalyticsR)
library(searchConsoleR)
library(lubridate)
library(jsonlite)
options(googleAuthR.client_id = "XXXX.apps.googleusercontent.com",
googleAuthR.client_secret = "XXX",
googleAuthR.scopes.selected = c("https://www.googleapis.com/auth/analytics.readonly",
"https://www.googleapis.com/auth/webmasters.readonly"))
gar_auth()
# Konfiguration -------------------------------------------------------
# Google Analytics View-ID ermitteln und eintragen
# ga_account_list() %>% View()
GA_VIEW_ID <- "XXX"
# GSC Property ermitteln und eintragen
# list_websites() %>% View()
GSC_PROP <- "XXX"
# Sisitrix
SISTRIX_DOMAIN <- "XXX"
SISTRIX_API_KEY <- "XXX"
# Start- und Endzeitpunkt der Datenabfragen definieren
# Hier: Sechs Monate rückwirkend vom Beginn des aktuellen Monats bis zum
# aktuellen Datum
START_DATE <- floor_date(today(), "month") %m-% months(6)
END_DATE <- today()
# Google Analytics ----------------------------------------------------
# Organic Search / Direct Sessions abfragen
channel_filter_organic <- dim_filter(dimension = "channelGrouping",
operator = "EXACT",
expressions = "Organic Search")
channel_filter_direct <- dim_filter(dimension = "channelGrouping",
operator = "EXACT",
expressions = "Direct")
filter_clause <- filter_clause_ga4(list(channel_filter_organic,
channel_filter_direct),
operator = "OR")
ga_sessions <- google_analytics(viewId = GA_VIEW_ID,
date_range = c(START_DATE, END_DATE),
metrics = c("sessions"),
dimensions = c("date", "channelGrouping"),
dim_filters = filter_clause,
anti_sample = TRUE)
# GSC -----------------------------------------------------------------
# GSA: Date
gsa_dim_date <- search_analytics(siteURL = GSC_PROP,
startDate = START_DATE,
endDate = END_DATE,
searchType = "web",
dimensions = "date")
# GSA: Date ~ Query
gsa_dim_date_query <- search_analytics(siteURL = GSC_PROP,
startDate = START_DATE,
endDate = END_DATE,
searchType = "web",
dimensions = c("date", "query"))
# GSA: Date ~ Page
gsa_dim_date_page <- search_analytics(siteURL = GSC_PROP,
startDate = START_DATE,
endDate = END_DATE,
searchType = "web",
dimensions = c("date", "page"))
# Sistrix -------------------------------------------------------------
# API-URLs zusammenbauen
si_url_desktop <- paste0("https://api.sistrix.com/domain.sichtbarkeitsindex?history=true&format=json&api_key=",
SISTRIX_API_KEY,
"&domain=",
SISTRIX_DOMAIN)
si_url_mobile <- paste0("https://api.sistrix.com/domain.sichtbarkeitsindex?history=true&format=json&api_key=",
SISTRIX_API_KEY,
"&domain=",
SISTRIX_DOMAIN,
"&mobile=true")
# API abfragen
api_response_desktop <- fromJSON(si_url_desktop)
api_response_mobile <- fromJSON(si_url_mobile)
# JSON-Antwort in DataFrame überführen
si_desktop <- api_response_desktop$answer$sichtbarkeitsindex[[1]]
si_mobile <- api_response_mobile$answer$sichtbarkeitsindex[[1]]
# Daten aufräumen 01 --------------------------------------------------
# Packages installieren; nur einmal nötig
# install.packages(c("tidyverse",
# "zoo",
# "padr"))
library(tidyverse)
library(zoo)
library(padr)
# GSC -----------------------------------------------------------------
# Impressions / Clicks
# Fehlende Datumspunkte einfügen, Impressions und
# Clicks mit 0 auffüllen
gsa_dim_date <- gsa_dim_date %>%
pad(start_val = START_DATE,
end_val = END_DATE) %>%
replace_na(list(clicks = 0,
impressions = 0))
# Gleitenden Mittelwert berechnen
gsa_dim_date <- gsa_dim_date %>%
mutate(rollmean_clicks = rollmean(x = clicks,
k = 7,
fill = NA,
align = "right"),
rollmean_impressions = rollmean(x = impressions,
k = 7,
fill = NA,
align = "right"))
# Tabelle transponieren
gsa_dim_date <- gsa_dim_date %>%
select(date, rollmean_clicks, rollmean_impressions) %>%
gather(key = "metric",
value = "value",
-date)
# Google Analytics ----------------------------------------------------
# Fehlende Datumspunkte einfügen und Sessions mit 0 auffüllen
ga_sessions <- ga_sessions %>%
pad(start_val = START_DATE,
end_val = END_DATE,
group = "channelGrouping") %>%
replace_na(list(sessions = 0))
# Gleitenden Mittelwert berechnen
ga_sessions <- ga_sessions %>%
group_by(channelGrouping) %>%
mutate(rollmean_sessions = rollmean(x = sessions,
k = 7,
fill = NA,
align = "right"))
# Daten aufräumen 02 --------------------------------------------------
# GSC -----------------------------------------------------------------
# Berechnung des Montags und Sonntags der Vorwoche
MONDAY_PREVIOUS_WEEK <- floor_date(today(), "week", 1) - 7
SUNDAY_PREVIOUS_WEEK <- MONDAY_PREVIOUS_WEEK + 6
# Page: Berechnung der Top 10 Seiten nach Clicks der Vorwoche
gsa_top_10_pages <- gsa_dim_date_page %>%
filter(between(date, MONDAY_PREVIOUS_WEEK, SUNDAY_PREVIOUS_WEEK)) %>%
group_by(page) %>%
summarise(sum_clicks = sum(clicks),
sum_impressions = sum(impressions),
ctr = sum_clicks / sum_impressions,
weight_position = sum(impressions * position) / sum(impressions)) %>%
ungroup() %>%
top_n(n = 10,
wt = sum_clicks) %>%
arrange(desc(sum_clicks)) %>%
mutate(page = str_replace(string = page,
pattern = GSC_PROP,
replacement = "/"),
ctr = round(x = ctr,
digits = 3),
weight_position = round(weight_position, 2)) %>%
rename("Page" = page,
"Clicks" = sum_clicks,
"Impressions" = sum_impressions,
"CTR" = ctr,
"Position" = weight_position)
# Query: Berechnung der Top 10 Queries nach Clicks der Vorwoche
gsa_top_10_queries <- gsa_dim_date_query %>%
filter(between(date, MONDAY_PREVIOUS_WEEK, SUNDAY_PREVIOUS_WEEK)) %>%
group_by(query) %>%
summarise(sum_clicks = sum(clicks),
sum_impressions = sum(impressions),
ctr = sum_clicks / sum_impressions,
weight_position = sum(impressions * position) / sum(impressions)) %>%
ungroup() %>%
top_n(10, sum_clicks) %>%
arrange(desc(sum_clicks)) %>%
mutate(ctr = round(ctr, 3),
weight_position = round(weight_position, 2)) %>%
rename("Query" = query,
"Clicks" = sum_clicks,
"Impressions" = sum_impressions,
"CTR" = ctr,
"Position" = weight_position)
# Sistrix -------------------------------------------------------------
si <- si_desktop %>%
mutate(date = date(date)) %>%
select(-domain) %>%
rename("Desktop" = value) %>%
left_join(si_mobile %>%
mutate(date = date(date)) %>%
select(-domain) %>%
rename("Mobile" = value),
by = "date") %>%
replace_na(list(Mobile = 0)) %>%
filter(between(date, START_DATE, END_DATE)) %>%
gather(key = "platform",
value = "value",
-date)