{"id":16575,"date":"2020-08-05T14:57:28","date_gmt":"2020-08-05T12:57:28","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=16575\/"},"modified":"2021-10-26T15:38:25","modified_gmt":"2021-10-26T13:38:25","slug":"velky-prehlad-grafov-v-google-data-studiu-pouzivate-ich-spravne","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/velky-prehlad-grafov-v-google-data-studiu-pouzivate-ich-spravne\/","title":{"rendered":"Ve\u013ek\u00fd preh\u013ead grafov v Google Data Studiu. Pou\u017e\u00edvate ich spr\u00e1vne?"},"content":{"rendered":"<p><b>Vizualiz\u00e1cia je ve\u013emi d\u00f4le\u017eitou s\u00fa\u010das\u0165ou takmer ka\u017edej anal\u00fdzy. <\/b><span style=\"font-weight: 400;\">Existuje ve\u013ek\u00e9 mno\u017estvo typov vizualiz\u00e1ci\u00ed, pri\u010dom ka\u017ed\u00e1 z nich je vhodn\u00e1 na zobrazenie in\u00e9ho typu d\u00e1t, popr\u00edpade na zobrazenie rozli\u010dn\u00fdch vz\u0165ahov medzi zobrazen\u00fdmi metrikami.<\/span><\/p>\n<h2><strong>Typy vizualiz\u00e1ci\u00ed v Google Data Studio<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Google Data Studio poskytuje hne\u010f nieko\u013eko typov grafov, ktor\u00e9 m\u00f4\u017eete vyu\u017ei\u0165 pri budovan\u00ed v\u00e1\u0161ho reportu. V tomto \u010dl\u00e1nku si prejdeme v\u0161etk\u00fdmi aktu\u00e1lne dostupn\u00fdmi typmi vizualiz\u00e1ci\u00ed v Google Data Studiu a uk\u00e1\u017eeme si, na ak\u00fd typ d\u00e1t je dan\u00e1 vizualiz\u00e1cia vhodn\u00e1.<\/span><\/p>\n<h3><strong>Preh\u013ead (Scorecard)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Preh\u013ead je najz\u00e1kladnej\u0161\u00ed typ vizualiz\u00e1cie, ke\u010f sa metrika zobraz\u00ed ako jedno \u010d\u00edslo. Viac o Preh\u013eadoch (Scorecards) sa do\u010d\u00edtate <\/span><a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/google-data-studio-prehlady-scorecards\/\"><span style=\"font-weight: 400;\">v tomto \u010dl\u00e1nku<\/span><\/a><span style=\"font-weight: 400;\">. Preh\u013eady nie je vhodn\u00e9 vyu\u017ei\u0165, pokia\u013e chcete rozdeli\u0165 metriku pod\u013ea kateg\u00f3ri\u00ed (napr\u00edklad, Rel\u00e1cie pod\u013ea source \/ medium). Osobne vyu\u017e\u00edvam Scorecardy najm\u00e4 ako doplnok k napr\u00edklad \u010ciarov\u00fdm grafom &#8211; zatia\u013e \u010do \u010diarov\u00fd graf zobrazuje trend v\u00fdvoja, Preh\u013ead zobraz\u00ed celkov\u00fa hodnotu metriky za vybran\u00e9 \u010dasov\u00e9 obdobie.<\/span><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter size-full wp-image-16576\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image1-31.png\" alt=\"Scorecards\" width=\"383\" height=\"72\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image1-31.png 383w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image1-31-300x56.png 300w\" sizes=\"(max-width: 383px) 100vw, 383px\" \/><\/p>\n<p style=\"text-align: center;\"><em><span style=\"font-weight: 400;\">Preh\u013eady (Scorecards).<\/span><\/em><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter size-full wp-image-16577\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image14-6.png\" alt=\"Kombin\u00e1cia scorecards a \u010diarov\u00e9ho grafu\" width=\"579\" height=\"291\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image14-6.png 579w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image14-6-300x151.png 300w\" sizes=\"(max-width: 579px) 100vw, 579px\" \/><\/p>\n<p style=\"text-align: center;\"><em><span style=\"font-weight: 400;\">Kombin\u00e1cia Preh\u013eadov a \u010ciarov\u00e9ho grafu.<\/span><\/em><\/p>\n<h3><strong>Tabu\u013eka (Table \/ Pivot table)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Tabu\u013eka je ve\u013emi roz\u0161\u00edren\u00fdm typom vizualiz\u00e1cie d\u00e1t. Po tabu\u013eke siahnite v\u017edy, ak potrebujete porovna\u0165 v\u00e4\u010d\u0161\u00ed po\u010det metr\u00edk, naprie\u010d viacer\u00fdmi dimenziami.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image8-10.png\" data-rel=\"lightbox-image-0\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16578 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image8-10.png\" alt=\"Tabu\u013eka\" width=\"541\" height=\"313\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image8-10.png 541w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image8-10-300x174.png 300w\" sizes=\"(max-width: 541px) 100vw, 541px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">\u0160peci\u00e1lnym typom tabu\u013eky je Pivot tabu\u013eka, v ktorej m\u00f4\u017eete zobrazi\u0165 dimenzie nie len v riadkoch, ale aj v st\u013apcoch. V pr\u00edpade, \u017ee pivot tabu\u013eku skombinujete s heat-mapou, dok\u00e1\u017eete v nej taktie\u017e zobrazi\u0165 vz\u0165ahy medzi jednoliv\u00fdmi metrikami a tak identifikova\u0165 najv\u00e4\u010d\u0161ie\/najmen\u0161ie hodnoty metr\u00edk ve\u013emi r\u00fdchlo.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> <a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image9-10.png\" data-rel=\"lightbox-image-1\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16579 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image9-10.png\" alt=\"Tabu\u013eka s heatmapou\" width=\"693\" height=\"294\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image9-10.png 693w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image9-10-300x127.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image9-10-600x255.png 600w\" sizes=\"(max-width: 693px) 100vw, 693px\" \/><\/a><\/span><\/p>\n<h3><strong>\u010ciarov\u00fd graf (Line chart \/ Time series)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">\u010ciarov\u00fd graf je vo v\u00e4\u010d\u0161ine vyu\u017e\u00edvan\u00fd na zobrazenie v\u00fdvoja metriky v \u010dase. Zobrazen\u00edm viacer\u00fdch metr\u00edk (pridan\u00edm \u010fal\u0161\u00edch \u010diar) viete metriky navz\u00e1jom porovna\u0165 a zobrazi\u0165 medzi nimi vz\u0165ah (napr. korel\u00e1ciu). Ke\u010f sa rozhodnete zobrazi\u0165 na \u010diarovom grafe viacero metr\u00edk, odpor\u00fa\u010dam nezobrazova\u0165 viac ako \u0161tyri, hlavne ak sa \u010diary na grafe prel\u00ednaj\u00fa. Taktie\u017e pou\u017eite dostato\u010dne kontrastn\u00e9 farby, aby bolo \u00faplne jasn\u00e9, ktor\u00e1 metrika zodpoved\u00e1 ktorej \u010diare na grafe.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image5-18.png\" data-rel=\"lightbox-image-2\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16580 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image5-18.png\" alt=\"\u010ciarov\u00fd graf s dvoma metrikami.\" width=\"387\" height=\"269\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image5-18.png 387w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image5-18-300x209.png 300w\" sizes=\"(max-width: 387px) 100vw, 387px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><i><span style=\"font-weight: 400;\">\u010ciarov\u00fd graf s dvoma metrikami.<\/span><\/i><\/p>\n<p style=\"text-align: left;\"><span style=\"font-weight: 400;\">V pr\u00edpade, \u017ee chcete zobrazi\u0165 dve metriky, pri\u010dom ka\u017ed\u00e1 z nich dosahuje r\u00e1dovo odli\u0161n\u00e9 hodnoty, m\u00f4\u017eete vyu\u017ei\u0165 \u010diarov\u00fd graf s dvoma y-ov\u00fdmi osami (pravou a \u013eavou). Napr\u00edklad, ak by ste chceli na jednom grafe zobrazi\u0165 konverzn\u00fd pomer (hodnoty men\u0161ie ako 1) a Rel\u00e1cie (r\u00e1dovo v desiatk\u00e1ch, stovk\u00e1ch \u010di tis\u00edcoch), \u010diara pre Konverzn\u00fd pomer by bola \u00faplne nevidite\u013en\u00e1. V takomto pr\u00edpade chcete rozdeli\u0165 y-ov\u00fa os na prav\u00fa a \u013eav\u00fa.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image4-22.png\" data-rel=\"lightbox-image-3\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16581 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image4-22.png\" alt=\"\" width=\"371\" height=\"282\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image4-22.png 371w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image4-22-300x228.png 300w\" sizes=\"(max-width: 371px) 100vw, 371px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><i><span style=\"font-weight: 400;\">Konverzn\u00fd pomer a Rel\u00e1cie vyu\u017e\u00edvaj\u00fa rovnak\u00fa (\u013eav\u00fa) y-ov\u00fa os.<\/span><\/i><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image6-16.png\" data-rel=\"lightbox-image-4\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16582 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image6-16.png\" alt=\"\" width=\"400\" height=\"264\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image6-16.png 400w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image6-16-300x198.png 300w\" sizes=\"(max-width: 400px) 100vw, 400px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><i><span style=\"font-weight: 400;\">Konverzn\u00fd pomer a Rel\u00e1cie vyu\u017e\u00edvaj\u00fa dve rozdielne y-ov\u00e9 osi.<\/span><\/i><\/p>\n<p>&nbsp;<\/p>\n<h3><strong>St\u013apcov\u00fd graf (Bar chart)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">St\u013apcov\u00fd graf je v\u0161estrann\u00fdm typom vizualiz\u00e1cie, preto zrejme patr\u00ed aj k t\u00fdm najpou\u017e\u00edvanej\u0161\u00edch. St\u013apcov\u00fd graf je vhodn\u00fd na zobrazenie ako distrib\u00facie, tak porovnania jednej alebo viacer\u00fdch hodn\u00f4t.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-30.png\" data-rel=\"lightbox-image-5\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16583 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-30.png\" alt=\"Uk\u00e1\u017eka st\u013apcov\u00e9ho grafu\" width=\"692\" height=\"226\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-30.png 692w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-30-300x98.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-30-600x196.png 600w\" sizes=\"(max-width: 692px) 100vw, 692px\" \/><\/a><\/p>\n<h3><strong>Kol\u00e1\u010dov\u00fd graf (Pie chart)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Kol\u00e1\u010dov\u00fd graf je taktie\u017e ve\u013emi roz\u0161\u00edren\u00fdm typom zobrazovania d\u00e1t. Ke\u010f sa rozhodnete pre kol\u00e1\u010dov\u00fd graf, je d\u00f4le\u017eit\u00e9 nezobrazova\u0165 pr\u00edli\u0161 ve\u013ea kateg\u00f3ri\u00ed, inak sa ve\u013emi r\u00fdchlo stane nepreh\u013eadn\u00fdm. Osobne nie som fan\u00fa\u0161ikom Kol\u00e1\u010dov\u00fdch grafov, preto\u017ee sa v\u017edy daj\u00fa nahradi\u0165 nejakou inou (vhodnej\u0161ou) alternat\u00edvou.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image3-28.png\" data-rel=\"lightbox-image-6\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16584 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image3-28.png\" alt=\"Kol\u00e1\u010dov\u00fd graf\" width=\"252\" height=\"236\" \/><\/a><\/p>\n<h3><strong>Mapa (Geo map \/ Google maps)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Pokia\u013e chcete zobrazi\u0165 distrib\u00faciu naprie\u010d r\u00f4znymi krajinami, zobrazenie metriky na mape je najlep\u0161ou vo\u013ebou. Zobrazenie funguje na princ\u00edpe Heat mapy, kde krajiny s najv\u00e4\u010d\u0161ou hodnotou s\u00fa zv\u00fdraznen\u00e9 najtmav\u0161\u00edm oddie\u0148om, zatia\u013e \u010do krajiny s najmen\u0161\u00edmi hodnotami s\u00fa najsvetlej\u0161ie. Navy\u0161e, v Google Data Studio m\u00f4\u017eete zobrazi\u0165 ako mapu cel\u00e9ho sveta, tak len konkr\u00e9tny svetadiel alebo dokonca len jeho \u010das\u0165.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image10-9.png\" data-rel=\"lightbox-image-7\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16585 size-large\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image10-9-1024x221.png\" alt=\"Uk\u00e1\u017eka mapovej vizualiz\u00e1cie.\" width=\"1024\" height=\"221\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image10-9-1024x221.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image10-9-300x65.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image10-9-600x130.png 600w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image10-9.png 1051w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/a><\/p>\n<h3><strong>Plo\u0161n\u00fd graf (Area)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Plo\u0161n\u00fd graf je vlastne \u010diarov\u00fd graf s t\u00fdm, \u017ee cel\u00e1 plocha medzi \u010diarou a x-ovou osou je vyplnen\u00e1 farbou.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Vari\u00e1ciou je Stacked Area chart, ktor\u00fd sa najm\u00e4 vyu\u017e\u00edva na zobrazenie zmeny viacer\u00fdch premenn\u00fdch v \u010dase.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image12-7.png\" data-rel=\"lightbox-image-8\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16586 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image12-7.png\" alt=\"Uk\u00e1\u017eka plo\u0161n\u00e9ho grafu\" width=\"709\" height=\"214\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image12-7.png 709w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image12-7-300x91.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image12-7-600x181.png 600w\" sizes=\"(max-width: 709px) 100vw, 709px\" \/><\/a><\/p>\n<h3><strong>Scatter (Scatter chart \/ Bubble chart)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Scatter chart je v\u00fdbornou vo\u013ebou, pokia\u013e chcete zobrazi\u0165 vz\u0165ah dvoch metr\u00edk, kde metrika A je na x-ovej a metrika B na y-ovej osi.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bubble chart je Scatter chart s tre\u0165ou metrikou, ktor\u00e1 je zn\u00e1zornen\u00e1 ve\u013ekos\u0165ou bodky. V Google Data Studio navy\u0161e m\u00f4\u017eete farbou bodky zn\u00e1zorni\u0165 r\u00f4zne dimenzie, \u010d\u00edm m\u00f4\u017eete zv\u00fdrazni\u0165 r\u00f4zne zoskupenia..<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image11-7.png\" data-rel=\"lightbox-image-9\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16587 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image11-7.png\" alt=\"\" width=\"731\" height=\"198\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image11-7.png 731w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image11-7-300x81.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image11-7-600x163.png 600w\" sizes=\"(max-width: 731px) 100vw, 731px\" \/><\/a><\/p>\n<h3><strong>Bullet (Bullet chart)<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Bullet chart sa v\u00e4\u010d\u0161inou pou\u017e\u00edva pri zobrazovan\u00ed nap\u013a\u0148ania clie\u013ea.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image13-7.png\" data-rel=\"lightbox-image-10\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16588 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image13-7.png\" alt=\"\" width=\"338\" height=\"186\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image13-7.png 338w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image13-7-300x165.png 300w\" sizes=\"(max-width: 338px) 100vw, 338px\" \/><\/a><\/p>\n<h3><strong>Treemap<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Treemap vizualiz\u00e1ica je vhodn\u00e1 na zobrazenie ako jednotliv\u00e9 komponenty prispievaj\u00fa do celku, pri\u010dom na rozdiel od Stacked Area Chart alebo Pie chart zobrazuje hne\u010f viacero celkov naraz.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image7-13.png\" data-rel=\"lightbox-image-11\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16589 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image7-13.png\" alt=\"\" width=\"468\" height=\"293\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image7-13.png 468w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image7-13-300x188.png 300w\" sizes=\"(max-width: 468px) 100vw, 468px\" \/><\/a><\/p>\n<h2><strong>Ak\u00fa vizualiz\u00e1ciu pou\u017ei\u0165?<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">U\u017e teda vieme, ak\u00e9 typy grafov n\u00e1m Google Data Studio pon\u00faka, ale pre ktor\u00fd z nich sa rozhodn\u00fa\u0165? Je ve\u013emi d\u00f4le\u017eit\u00e9 zvoli\u0165 graf, ktor\u00fd \u010do najlep\u0161ie odprezentuje va\u0161e z\u00e1very. Ja osobne som si osvojil princ\u00edp Dr. Andrewa Abela. V\u0161etky vizualiza\u010dn\u00e9 techniky rozdelil do \u0161tyroch skup\u00edn pod\u013ea toho, na \u010do je dan\u00e1 vizualiz\u00e1cia vhodn\u00e1:<\/span><\/p>\n<ul>\n<li>Porovnanie<\/li>\n<li>Distrib\u00faciu<\/li>\n<li>Kompoz\u00edciu<\/li>\n<li>Z\u00e1vislos\u0165<\/li>\n<\/ul>\n<h2><b>Google Data Studio Cheat Sheet<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Nakoniec som pre v\u00e1s pripravil <\/span><a href=\"https:\/\/datastudio.google.com\/reporting\/15c2YlY9SLyMcb7vQiFu-v8tjoFzIyCab\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"font-weight: 400;\">cheat sheet<\/span><\/a><span style=\"font-weight: 400;\"> v podobe Google Data Studio Reportu, ktor\u00fd zobrazuje v\u0161etky aktu\u00e1lne dostupn\u00e9 typy vizualiz\u00e1cii. Nasledovan\u00edm rovnak\u00fdch krokov v\u00e1m pom\u00f4\u017ee vybra\u0165 presne ten graf, ktor\u00fd najlep\u0161ie odprezentuje to, \u010do potrebujete.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ak\u00fd typ vizualiz\u00e1cie v\u00e1m ch\u00fdba v Google Data Studio? Pode\u013ete sa s nami dolu v koment\u00e1roch.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Vizualiz\u00e1cia je ve\u013emi d\u00f4le\u017eitou s\u00fa\u010das\u0165ou takmer ka\u017edej anal\u00fdzy. Existuje ve\u013ek\u00e9 mno\u017estvo typov vizualiz\u00e1ci\u00ed, pri\u010dom ka\u017ed\u00e1 z nich je&#8230;<\/p>\n","protected":false},"author":62,"featured_media":16596,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[667],"tags":[668,793,792],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/16575"}],"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\/62"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=16575"}],"version-history":[{"count":8,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/16575\/revisions"}],"predecessor-version":[{"id":17941,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/16575\/revisions\/17941"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/16596"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=16575"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=16575"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=16575"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}