Make separate examples proper R-style demos
Change-Id: I0ac284cfc1d0c508030c91189f260299680c1485
diff --git a/demo/00Index b/demo/00Index
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+frequenciesOverTime Plot frequency of query expressions over time
+frequenciesOverDomains Box plot frequency of query expressions per topic domain
+conditionsOverTime Plot frequency of query expressions over time under different conditions
+alternativesOverTime Plot proportion of alternative spellings/variants over time
+regional Map plot regional frequencies of query expression
diff --git a/demo/alternativesOverTime.R b/demo/alternativesOverTime.R
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+#!/usr/bin/env Rscript
+#
+# Plot frequency of alternative expressions or spellings variants over time
+#
+library(RKorAPClient)
+library(ggplot2)
+library(reshape2)
+library(plotly)
+library(htmlwidgets)
+
+alternativesOverTime <- function(alternatives, years, kco = new("KorAPConnection", verbose=TRUE)) {
+ df = data.frame(year=years)
+ vc = "textType = /Zeit.*/ & pubDate in"
+ urls <- data.frame()
+ for (v in alternatives) {
+ df[v] <- sapply(df$year, function(y) {
+ kqo <- corpusQuery(kco, query=v, vc=paste(vc, y))
+ urls <<- rbind(urls, data.frame(Variant=v, year=y, url=kqo@webUIRequestUrl))
+ kqo@totalResults
+ })
+ }
+ df$total <- apply(df[,alternatives], 1, sum)
+ df <- merge(melt(df, measure.vars = alternatives, value.name = "afreq", variable.name = "Variant"),
+ urls, by=c("Variant", "year"))
+ df$ci <- t(sapply(Map(prop.test, df$afreq, df$total), "[[","conf.int"))
+ df$share <- df$afreq / df$total
+ g <- ggplot(data = df, mapping = aes(x = year, y = share, color=Variant, fill=Variant)) +
+ geom_ribbon(aes(ymin=ci[, 1], ymax=ci[, 2], color=Variant, fill=Variant), alpha=.3, linetype=0) +
+ geom_line() +
+ geom_point() +
+ ggtitle(paste0(alternatives, collapse = " vs. ")) +
+ xlab("TIME") +
+ ylab(sprintf("Observed frequency ratio")) +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_x_continuous(breaks=unique(df$year))
+ pp <- ggplotly(g, tooltip = c("x", "y"))
+ for (i in 1:length(alternatives)) {
+ vdata <- df[df$Variant==alternatives[i],]
+ pp$x$data[[2+i]]$customdata <- vdata$url
+ pp$x$data[[2+i]]$text <- sprintf("%s<br />absolute: %d / %d", pp$x$data[[2+i]]$text, vdata$afreq, vdata$total)
+ }
+ ppp <- onRender(pp, "function(el, x) { el.on('plotly_click', function(d) { var url=d.points[0].customdata; window.open(url, 'korap') })}")
+ print(ppp)
+ df
+}
+
+df <- alternativesOverTime(c('so "genannte.?"', '"sogenannte.?"'), (1995:2018))
diff --git a/demo/conditionsOverTime.R b/demo/conditionsOverTime.R
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+++ b/demo/conditionsOverTime.R
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+#!/usr/bin/env Rscript
+#
+# Plot frequency of an expressions under multiple conditions over time
+#
+#library(devtools)
+#install_git("https://korap.ids-mannheim.de/gerrit/KorAP/RKorAPClient", upgrade="never")
+library(RKorAPClient)
+library(ggplot2)
+library(reshape2)
+#library(plotly)
+
+conditionsOverTime <- function(query, conditions, years, kco = new("KorAPConnection", verbose = TRUE)) {
+ df = data.frame(year=years)
+ for (c in conditions) {
+ df[c] <- sapply(df$year, function(y)
+ corpusQuery(kco, query, vc=paste(c, "& pubDate in", y))@totalResults)
+
+ }
+ df <- melt(df, measure.vars = conditions, value.name = "afreq", variable.name = "condition")
+ df$total <- apply(df[,c('year','condition')], 1, function(x) corpusStats(kco, vc=paste(x[2], "& pubDate in", x[1]))@tokens )
+ df$ci <- t(sapply(Map(prop.test, df$afreq, df$total), "[[","conf.int"))
+ df$freq <- df$afreq / df$total
+ g <- ggplot(data = df, mapping = aes(x = year, y = freq, fill=condition, color=condition)) +
+ geom_point() +
+ geom_line() +
+ geom_ribbon(aes(ymin=ci[, 1], ymax=ci[, 2], fill=condition, color=condition), alpha=.3, linetype=0) +
+ xlab("TIME") +
+ labs(color="Virtual Corpus", fill="Virtual Corpus") +
+ ylab(sprintf("Observed frequency of \u201c%s\u201d", query)) +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_x_continuous(breaks=unique(df$year))
+ print(g)
+ # print(ggplotly(g, tooltip = c("x", "y")))
+
+ df
+}
+
+df <- conditionsOverTime("[tt/l=Heuschrecke]", c("textClass = /natur.*/", "textClass=/politik.*/", "textClass=/wirtschaft.*/"), (2002:2018))
+#df <- conditionsOverTime("wegen dem [tt/p=NN]", c("textClass = /sport.*/", "textClass=/politik.*/", "textClass=/kultur.*/"), (1995:2005))
diff --git a/demo/data/regions.rds b/demo/data/regions.rds
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index 0000000..cee9038
--- /dev/null
+++ b/demo/data/regions.rds
Binary files differ
diff --git a/demo/frequenciesOverDomains.R b/demo/frequenciesOverDomains.R
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+++ b/demo/frequenciesOverDomains.R
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+#!/usr/bin/env Rscript
+#
+# Plot frequency of query expressions per topic domain
+#
+library(RKorAPClient)
+library(ggplot2)
+
+freqPerDomain <- function(query, con = new("KorAPConnection", verbose = TRUE)) {
+ q <- corpusQuery(con, query = query, vc="")
+ q <- fetchAll(q)
+ tokensPerMainTopic <-
+ function(topic) {
+ return(corpusStats(con, sprintf("textClass = /%s.*/", topic))@tokens)
+ }
+ q@collectedMatches$primaryTopic <-
+ sapply(strsplit(as.character(q@collectedMatches$textClass), " "), `[[`, 1)
+ df <- as.data.frame(table(q@collectedMatches$primaryTopic, dnn = "Domain"))
+ df$total <- sapply(df$Domain, tokensPerMainTopic)
+ df$freq <- df$Freq / df$total
+ df$ci <- t(sapply(Map(prop.test, df$Freq, df$total), "[[","conf.int"))
+ g <- ggplot(data = df, mapping = aes(x = Domain, y = freq)) +
+ geom_col() +
+ geom_errorbar(aes(ymin=ci[, 1], ymax=ci[, 2]), width=.5, alpha=.5) +
+ ylab(sprintf("Observed frequency of \u201c%s\u201d", query)) +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
+ print(g)
+ df
+}
+df <- freqPerDomain("Hatespeech")
+
diff --git a/demo/frequenciesOverTime.R b/demo/frequenciesOverTime.R
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+++ b/demo/frequenciesOverTime.R
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+#!/usr/bin/env Rscript
+#
+# Plot frequency of query expressions over time
+#
+library(RKorAPClient)
+library(ggplot2)
+
+freqPerYear <- function(query, con = new("KorAPConnection", verbose = TRUE)) {
+ vc <- "pubDate since 2000 & pubDate until 2018 & textType = /Zeit.*/"
+ q <- corpusQuery(con, query = query, vc=vc)
+ q <- fetchAll(q)
+ tokensPerYear <- function(year) {
+ return(corpusStats(con, sprintf("%s & pubDate in %s", vc, year))@tokens)
+ }
+ df <- as.data.frame(table(as.numeric(format(q@collectedMatches$pubDate,"%Y")), dnn="year"),
+ stringsAsFactors = FALSE)
+ df <- merge(data.frame(year=min(df$year):max(df$year)), df, all = TRUE)
+ df[is.na(df$Freq),]$Freq <- 0
+ df$total <- sapply(df$year, tokensPerYear)
+ df$freq <- df$Freq / df$total
+ df$ci <- t(sapply(Map(prop.test, df$Freq, df$total), "[[","conf.int"))
+ g <- ggplot(data = df, aes(x = year, y = freq, group=1)) +
+ geom_ribbon(aes(ymin=ci[, 1], ymax=ci[, 2]), alpha=.3) +
+ geom_point() +
+ geom_line() +
+ xlab("TIME") +
+ ylab(sprintf("Observed frequency of \u201c%s\u201d", query)) +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
+ print(g)
+ df
+}
+#df <- freqPerYear("Car-Bikini")
+#df <- freqPerYear("[tt/p=ART & opennlp/p=ART] [tt/l=teilweise] [tt/p=NN]")
+df <- freqPerYear("Buschzulage")
+
diff --git a/demo/regional.R b/demo/regional.R
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+++ b/demo/regional.R
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+#!/usr/bin/Rscript
+library(RKorAPClient)
+library(ggplot2)
+library(raster)
+library(broom)
+library(plotly)
+library(htmlwidgets)
+
+devAskNewPage(ask = FALSE)
+mapfile <- "demo/data/cache/map-v2.rds"
+
+fetchAndPrepareMap <- function(map, pick) {
+ cat("Downloading GADM map data for ", map, "\n")
+ sp <- readRDS(url(sprintf("https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_%s_sp.rds", map)))
+ if (pick > 0) {
+ sp@polygons <- sp@polygons[pick]
+ sp@data <- sp@data[pick,]
+ }
+ sp
+}
+
+fetchMaps <- function(maps, picks) {
+ if (file.exists(mapfile)) {
+ df <- readRDS(mapfile)
+ } else {
+ cat("Downloading and caching GADM map data.\nPlease note that the GADM map data is licensed for academic use and other non-commercial use, only.\nSee https://gadm.org/license.html\n")
+ df <- broom::tidy(Reduce(bind, mapply(fetchAndPrepareMap, maps, picks)))
+ dir.create(dirname(mapfile), recursive = TRUE, showWarnings = FALSE)
+ saveRDS(df, mapfile)
+ }
+ df$grp <- floor(as.numeric(as.character(df$group)))
+ df
+}
+
+map <- fetchMaps(c("DEU_1", "AUT_0", "CHE_0", "LUX_0", "BEL_3", "ITA_1", "LIE_0"), c(0, 0, 0, 0, 34, 17, 0))
+
+geoDistrib <- function(query, kco = new("KorAPConnection", verbose=TRUE)) {
+ regions <- readRDS("demo/data/regions.rds")
+ regions$freq <- NA
+ regions$url <- NA
+ plot <- NULL
+ vc <- ""
+ for (i in 1:nrow(regions)) {
+ if (!is.na(regions[i,]$query)) {
+ cat(as.character(regions[i,]$region), "\n")
+ regions[i,]$total <- corpusStats(kco, vc=paste0(vc, regions[i,]$query))@tokens
+ if (regions[i,]$total == 0) {
+ regions[i,]$afreq <- 0
+ regions[i,]$freq <- NA
+ } else {
+ kqo <- corpusQuery(kco, query, vc=paste0(vc, regions[i,]$query))
+ regions[i,]$afreq <- kqo@totalResults
+ regions[i,]$freq <- regions[i,]$afreq / regions[i,]$total
+ regions[i,]$url <- kqo@webUIRequestUrl
+ }
+ cat(regions[i,]$afreq, regions[i,]$total, regions[i,]$freq, "\n")
+ plot <- updatePlot(query, map, regions)
+ cat("\n\n")
+ }
+ }
+ pp <- ggplotly(plot)
+ for (i in 1:nrow(regions)) {
+ j <- grep(paste0(regions$region[i], "\""), pp$x$data, perl=TRUE)
+ pp$x$data[[j]]$customdata <- regions[i,]$url
+ }
+ ppp <- onRender(pp, "function(el, x) { el.on('plotly_click', function(d) { var url=d.points[0].data.customdata; window.open(url, 'korap') })}")
+ print(ppp)
+ pp
+}
+
+updatePlot <- function(query, map, regions) {
+ map$ipm <- sapply(map$grp, function(grp) regions$freq[grp] * 10^6)
+ map$region <- sapply(map$grp, function(grp) regions$region[grp])
+ map$url <- sapply(map$grp, function(grp) regions$url[grp])
+ regionsPlot <- ggplot(map) +
+ geom_polygon(aes(x=long, y=lat, group=group, fill=ipm, text=region), colour= "black", size=.1) +
+ theme(axis.line.x = element_blank(),
+ axis.line.y = element_blank(),
+ panel.grid.major = element_blank(),
+ panel.grid.minor = element_blank(),
+ panel.border = element_blank(),
+ panel.background = element_blank(),
+ axis.line=element_blank(),axis.text.x=element_blank(),
+ axis.text.y=element_blank(),axis.ticks=element_blank(),
+ axis.title.x=element_blank(),
+ axis.title.y=element_blank()) +
+ coord_equal(ratio=1.5) +
+ labs(title = sprintf("Regional distribution of \u201c%s\u201d", query))
+ print(regionsPlot)
+ regionsPlot
+}
+
+#geoDistrib("wegen dem [tt/p=NN]")
+geoDistrib("heuer")
+#geoDistrib("Sonnabend")
+#geoDistrib("eh")