114 lines
3.8 KiB
R
114 lines
3.8 KiB
R
source("common.R")
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# Beschreibung der Punktzahl
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summary(sites$score)
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# Punkte
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## Verteilung der Punkte
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ggplot(sites, aes(score)) +
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geom_histogram(bins = 15) +
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labs(title="Verteilung der Punktzahlen", x="Punktzahl", y="Anzahl Sites")
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ggsave("plots/punkte_verteilung.png")
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## Verteilung der Punktzahlen (nur KV)
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summary(sites_kv$score)
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ggplot(sites_kv, aes(score)) +
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geom_histogram(bins = 15) +
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labs(title="Verteilung der Punktzahlen (nur KV)", x="Punktzahl", y="Anzahl Sites")
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ggsave("plots/punkte_verteilung_kv.png")
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## Verteilung der Punktzahlen (nur OV)
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summary(sites_ov$score)
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ggplot(sites_ov, aes(score)) +
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geom_histogram(bins = 15) +
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labs(title="Verteilung der Punktzahlen (nur OV)", x="Punktzahl", y="Anzahl Sites")
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ggsave("plots/punkte_verteilung_ov.png")
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## Punkte nach Gliederungsebene
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ggplot(sites, aes(x = reorder(meta.level, score, FUN=median), y = score)) +
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coord_flip() +
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geom_boxplot() +
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labs(title="Punktzahl nach Gliederungsebene", x="Gliederung", y="Punkte")
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ggsave("plots/punkte_nach_gliederungsebene.png")
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## Punkte KV nach Bundesland
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ggplot(sites_kv, aes(x=reorder(meta.state, -score, FUN=median), y=score)) +
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coord_flip() +
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geom_boxplot() +
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labs(title="Punktzahl nach Bundesland", x="Bundesland", y="Punkte")
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ggsave("plots/punkte_kv_nach_bundesland.png")
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## Punkte nach CMS
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ggplot(sites_top_cms, aes(x=reorder(generator, -score, FUN=median), y=score)) +
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coord_flip() +
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geom_boxplot() +
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labs(title="Punktzahl nach CMS", y="Punkte", x="CMS")
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ggsave("plots/punkte_nach_cms.png")
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## Einzelne Site-Kriterien
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# Feeds nach CMS
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ggplot(sites_top_cms, aes(rating.FEEDS.value)) +
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geom_bar(aes(fill = generator)) +
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scale_x_discrete(labels=c("Ohne Feed","Mit Feed")) +
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scale_fill_discrete(name = "CMS") +
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labs(title="Sites mit und ohne Feed nach CMS",
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x="",
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y="Anzahl Sites")
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ggsave("plots/feeds_nach_cms_a.png")
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ggplot(sites_top_cms, aes(generator)) +
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geom_bar(aes(fill = rating.FEEDS.value)) +
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coord_flip() +
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scale_fill_discrete(name = "Feed vorhanden", labels = c("Nein", "Ja")) +
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labs(title="Sites mit und ohne Feed nach CMS",
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x="",
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y="Anzahl Sites")
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ggsave("plots/feeds_nach_cms_b.png")
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## Alle Kriterien
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sites_rating_criteria <- select(sites,
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rating.DNS_RESOLVABLE_IPV4.value,
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rating.SITE_REACHABLE.value,
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rating.CANONICAL_URL.value,
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rating.WWW_OPTIONAL.value,
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rating.HTTPS.value,
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rating.RESPONSIVE.value,
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rating.CONTACT_LINK.value,
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rating.SOCIAL_MEDIA_LINKS.value,
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rating.NO_NETWORK_ERRORS.value,
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rating.NO_SCRIPT_ERRORS.value,
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rating.NO_THIRD_PARTY_COOKIES.value,
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rating.FAVICON.value,
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rating.USE_SPECIFIC_FONTS.value,
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rating.FEEDS.value,
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)
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sites_rating_criteria_long <- tidyr::gather(sites_rating_criteria, key = type_col, value = categories)
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sapply(sites_rating_criteria_long,table)
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# requires library(qdapTools)
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mtabulate(sites_rating_criteria_long)
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(sum(sites_rating_criteria$rating.FEEDS.value) / count(sites)) * 100
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(sum(sites_rating_criteria$rating.SOCIAL_MEDIA_LINKS.value) / count(sites)) * 100
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(sum(sites_rating_criteria$rating.RESPONSIVE.value) / count(sites)) * 100
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(sum(sites_rating_criteria$rating.NO_THIRD_PARTY_COOKIES.value) / count(sites)) * 100
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ggplot(sites_rating_criteria_long, aes(x = categories, fill = categories)) +
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geom_bar() +
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coord_flip() +
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facet_wrap(~ type_col, scales = "free_x")
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ggplot(sites_rating_criteria_long, aes(x = categories, fill = categories)) +
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geom_bar()
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