n\u00e1jdete zoznam v\u0161etk\u00fdch dostupn\u00fdch dimenzi\u00ed a metrik v Google Analytics. Pam\u00e4tajte, \u017ee ak pou\u017e\u00edvate kni\u017enicu googleAnalyticsR, v\u0161etky dimenzie a metriky d\u00e1vate bez predpony <\/span>ga:<\/b><\/p>\nNOTE 2: <\/b>Rovnako v\u0161etky dimenzie a metriky viete zobrazi\u0165 aj v rozhran\u00ed R Studia, a to pomocou pr\u00edkazu <\/span>google_analytics_meta()<\/b><\/p>\nOdpor\u00fa\u010dam si to tie\u017e priradi\u0165 do premennej, napr\u00edklad takto:<\/span><\/p>\nmeta <- google_analytics_meta()<\/span><\/p>\nTak a pokra\u010dujme \u010falej k n\u00e1\u0161mu prv\u00e9mu query. \ud83d\ude42<\/span><\/p>\n# Pull the data. This is set to pull the last 90 days of data.<\/span><\/p>\ngadata <- google_analytics_4(view_id, <\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0date_range = c(NdaysAgo, yesterday),<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0metrics = c(„sessions“), <\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0dimensions = c(„week“, „deviceCategory“),<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0anti_sample = TRUE)<\/span><\/p>\nTeraz si vysvetl\u00edme, \u010do ka\u017ed\u00fd k\u013e\u00fa\u010d a hodnota znamenaj\u00fa.<\/span><\/p>\n![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image9-2.png\")
<\/p>\n
Ke\u010f vy\u0161\u0161ie spomenut\u00e9 query vlo\u017e\u00edte do pr\u00edkazov\u00e9ho riadku v RStudio, ulo\u017e\u00ed v\u00e1m v\u00fdsledky do dataframu s n\u00e1zvom <\/span>gadata<\/b>. <\/span><\/p>\n![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image4-7.png\")
<\/p>\n
V\u017edy je n\u00e1pomocn\u00e9, ke\u010f pou\u017eijete<\/span> funkciu head(), ktor\u00e1 v\u00e1m uk\u00e1\u017ee nieko\u013eko riadkov z va\u0161ich d\u00e1t<\/b>. M\u00f4\u017eete si pozrie\u0165, \u010di m\u00e1 tabu\u013eka v\u0161etky dimenzie a metriky, ktor\u00e9 ste chceli. Ide o ve\u013emi v\u010fa\u010dn\u00fa funkciu, ktor\u00e1 v\u00e1m r\u00fdchlo uk\u00e1\u017ee, ak\u00e9 d\u00e1ta m\u00e1te v tabu\u013eke.<\/span><\/p>\nPo kliknut\u00ed na tabu\u013eku sa v\u00e1m otvor\u00ed nov\u00e9 okno s n\u00e1zvom va\u0161ej tabu\u013eky.<\/span><\/p>\n![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image1-9.png\")
<\/p>\n
Gratulujem, pr\u00e1ve ste vytvorili svoje prv\u00e9 query a vytiahli d\u00e1ta z Google Analytics. Princ\u00edp je v\u017edy rovnak\u00fd. Akur\u00e1t si d\u00e1vajte v\u017edy <\/span>pozor na to, aby ste nemie\u0161ali metriky a dimenzie<\/b>, ktor\u00e9 maj\u00fa odli\u0161n\u00e9 scopes (rozsahy). <\/span>V\u00fdsledkom m\u00f4\u017eu by\u0165 d\u00e1ta, ktor\u00e9 s\u00fa nepresn\u00e9<\/b> (hoci v\u00e1m to v\u017edy vyhod\u00ed nejak\u00e9 \u010d\u00edsla).<\/span><\/p>\nVizualiz\u00e1cia d\u00e1t z Google Analytics<\/span><\/h2>\nTeraz, ke\u010f ste si vytiahli nevzorkovan\u00e9 (unsampled) d\u00e1ta z Google Analytics, je d\u00f4le\u017eit\u00e9 vizualizova\u0165 ich. Takto m\u00f4\u017eete lep\u0161ie porozumie\u0165 d\u00e1tam za dan\u00e9 \u010dasov\u00e9 obdobie. V R je jedine\u010dn\u00fdm pomocn\u00edkom kni\u017enica <\/span>ggplot.<\/b> Verte, \u017ee ak ste (alebo budete) nad\u0161enec pre R a Google Analytics ako ja, ggplot sa stane jedn\u00fdm z va\u0161ich najlep\u0161\u00edch kamar\u00e1tov.<\/span><\/p>\n\u010co potrebujete spravi\u0165, aby ste mohli t\u00fato kni\u017enicu pou\u017e\u00edva\u0165? Mus\u00edte pou\u017ei\u0165 rovnak\u00fd pr\u00edkaz ako na za\u010diatku s kni\u017enicou googleAnalyticsR, \u00a0len teraz pou\u017eijete ggplot.<\/span><\/p>\ninstall.packages(„ggplot2“)<\/span><\/p>\nlibrary(ggplot2)<\/span><\/p>\nT\u00fdmto pr\u00edkazom spust\u00edte ggplot vo va\u0161om workspace. N\u00e1sledne m\u00f4\u017eete <\/span>spravi\u0165 va\u0161u prv\u00fa vizualiz\u00e1ciu d\u00e1t z Google Analytics pomocou ggplot kni\u017enice<\/b>.<\/span><\/p>\n# Boxplot visualization bz weeks for last 90 days<\/span><\/p>\ngg <- ggplot(gadata, aes(x=week, y=sessions)) + geom_boxplot()<\/span><\/p>\ngg<\/span><\/p>\nPo zadan\u00ed vy\u0161\u0161ie spomenut\u00e9ho pr\u00edkazu do R konzoly, v\u00e1m program vytvor\u00ed nasleduj\u00faci graf. V mojom pr\u00edpade som pou\u017eil boxplot, preto\u017ee som chcel vidie\u0165 <\/span>rozdiely medzi minim\u00e1lnymi a maxim\u00e1lnymi hodnotami za dan\u00fd t\u00fd\u017ede\u0148<\/b> po\u010das posledn\u00fdch 3 mesiacov.<\/span><\/p>\n![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image3-9.png\")
<\/p>\n
Sk\u00fasme teraz tro\u0161ku zmeni\u0165 na\u0161e prv\u00e9 query, kde budeme <\/span>zis\u0165ova\u0165 pomer medzi pageviewsPerSession a po\u010dtom dan\u00fdch sessions<\/b>. N\u00e1sledne vytvor\u00edme segmenty na z\u00e1klade zariadenia (desktop,mobil, tablet) a \u010fal\u0161\u00ed graf na z\u00e1klade dimenzie m\u00e9dium. Toto v\u0161etko m\u00f4\u017eeme spravi\u0165 v jednom skripte aj s vizualiz\u00e1ciou.<\/span><\/p>\nNajsk\u00f4r treba prida\u0165 do vektoru metrics \u010fal\u0161iu metriku, ktorou je <\/span>pageviewsPerSession<\/b>.<\/span> Zab\u00fada\u0165 netreba ani na dimenzie <\/span>date<\/b> a <\/span>medium<\/b>.<\/span><\/p>\n# Pull the data. This is set to pull the last 90 days days of data.<\/span><\/p>\ngadata <- google_analytics_4(view_id, <\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0date_range = c(NdaysAgo, yesterday),<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0metrics = c(„sessions“,“pageviewsPerSession“), \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0dimensions = c(„date“,“medium“,“deviceCategory“),<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0max = -1)<\/span><\/p>\ngadata<\/span><\/p>\n <\/p>\n
gadata_viz <- ggplot(gadata10o, aes(x = sessions, y = pageviewsPerSession)) + geom_point() + facet_grid(~deviceCategory)<\/span><\/p>\ngadata_viz<\/span><\/p>\n![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image6-3.png\")
<\/p>\n
Ak chcete urobi\u0165 podobn\u00fd graf, ako je vy\u0161\u0161ie, len so segmentom medium, netreba u\u017e robi\u0165 nov\u00fd query, preto\u017ee dan\u00e9 hodnoty u\u017e m\u00e1te v tabu\u013eke. Sta\u010d\u00ed len upravi\u0165 pr\u00edkaz v ggplot.<\/span><\/p>\nOp\u00e4\u0165 pou\u017eijete dataframe <\/span>gadata, <\/b>ale tentokr\u00e1t uprav\u00edte face grid. Nie na device category, ale medium. Uprav\u00edte len t\u00fa \u010das\u0165, ktor\u00e1 je vyfarben\u00e1.<\/span><\/p>\nPredt\u00fdm<\/b><\/p>\n
gadata_viz <- ggplot(gadata, aes(x = sessions, y = pageviewsPerSession)) + geom_point() + <\/span>facet_grid(~deviceCategory)<\/span><\/p>\ngadata_viz<\/span><\/p>\n <\/p>\n
Potom<\/b><\/p>\n
gadata_viz <- ggplot(gadata10o, aes(x = sessions, y = pageviewsPerSession)) + geom_point() + facet_grid(~medium)<\/span><\/p>\ngadata_viz<\/span><\/p>\n![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-9.png\")
<\/p>\n
Ka\u017edop\u00e1dne tento graf je ve\u013emi \u0165a\u017eko \u010ditate\u013en\u00fd ke\u010f\u017ee je tam ve\u013ea premenn\u00fdch. Tak si ho po\u010fme tro\u0161ka upravi\u0165 do tak\u00e9ho form\u00e1tu aby to bolo prieh\u013eadnej\u0161ie.<\/p>\n
filtered_data <- gadata10o[gadata10o$sessions > 30 ,]<\/p>\n
Na grafe na prv\u00fd poh\u013ead vid\u00edme, \u017ee je preh\u013eadnej\u0161\u00ed a \u010ditatelnej\u0161\u00ed pou\u017e\u00edvame len ur\u010dit\u00fa podmno\u017einu \u00fadajov na z\u00e1klade filtru, ktor\u00fd sme vytvorili vy\u0161\u0161ie.<\/p>\n
![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/Rplot03.png\")
<\/p>\n
Pr\u00edkaz vy\u0161\u0161ie n\u00e1m pom\u00e1ha vyfiltrova\u0165 len tie kan\u00e1ly, ktor\u00e9 maj\u00fa denne n\u00e1v\u0161tevnos\u0165 vy\u0161\u0161iu ako 30 rel\u00e1cii. Samozrejme logiku filtru si nastav\u00ed ka\u017ed\u00fd s\u00e1m pod\u013ea va\u0161ich cie\u013eov \u010do chcete minim\u00e1lne dosiahnu\u0165.<\/p>\n
Z\u00e1ver<\/span><\/h2>\nV \u010dl\u00e1nku sme si uk\u00e1zali, <\/span>ako vytvori\u0165 jednoduch\u00e9 query, aby sme dostali nevzorkovan\u00e9 d\u00e1ta z Google Analytics<\/b>. D\u00f4le\u017eit\u00e9 je pochopi\u0165 princ\u00edp pr\u00e1ce s R, ako aj samostatnou kni\u017enicou googleAnalyticsR. Ak viete spr\u00e1vne zostavi\u0165 query, potom je to u\u017e len na va\u0161ej potrebe, ak\u00e9 d\u00e1ta chcete vytiahnu\u0165 z Google Analytics a n\u00e1sledne ich spracova\u0165 \u010di analyzova\u0165. <\/span><\/p>\nV\u00fdhodou pr\u00e1ce R a Google Analytics je, \u017ee <\/span>viete automatizova\u0165 monot\u00f3nne \u00falohy pomocou zop\u00e1r pr\u00edkazov<\/b>. Osobne vyu\u017e\u00edvam R a Google Analytics na <\/span>automatizovan\u00fd audit, automatizovan\u00e9 anal\u00fdzy a preh\u013ead v\u00fdkonu str\u00e1nky, ako aj automatizovan\u00e9 prid\u00e1vanie filtrov, cie\u013eov alebo vlastn\u00fdch dimenzi\u00ed \u010di metr\u00edk<\/b>.<\/span><\/p>\nVyu\u017eite\u013enos\u0165 R s Google Analytics skuto\u010dne nepozn\u00e1 hran\u00edc <\/span>\u2013 <\/span>od deskript\u00edvnej analytiky a\u017e po segment\u00e1ciu z\u00e1kazn\u00edkov. O tom si \u00a0viac nap\u00ed\u0161eme v nasleduj\u00facom \u010dl\u00e1nku. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Google Analytics je skvel\u00fdm n\u00e1strojom pre za\u010diato\u010dn\u00edkov, ako aj pokro\u010dil\u00fdch. Samotn\u00fd Google rob\u00ed mnoho pre lep\u0161iu u\u017e\u00edvate\u013enos\u0165 a…<\/p>\n","protected":false},"author":66,"featured_media":14097,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[200],"tags":[],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14080"}],"collection":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/users\/66"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=14080"}],"version-history":[{"count":5,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14080\/revisions"}],"predecessor-version":[{"id":14098,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14080\/revisions\/14098"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/14097"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=14080"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=14080"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=14080"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image7-2.png\")
<\/p>\n![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image8-2.png\")
<\/p>\n![\"\"](\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image5-4.png\")
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