mc <- df[unique(c(grep("Magna", df$publication.title),grep("Carta", df$publication.title), grep("Chart", df$publication.title))),]
library(ggplot2) library(dplyr) theme_set(theme_bw(50)) selected.authors <- c("prynne, william (1600-1669)", "defoe, daniel (1661-1731)", "hume, david (1711-1776)", "hume, david (1560-1630)") top <- names(rev(sort(table(df$author.unique))))[1:20] top <- c(top, "hume, david (1560-1630)") top <- setdiff(top, "caesar, julius (-100- -44)") dfs <- filter(df, author.unique %in% top) p <- top_plot(dfs, "author.unique", ntop = 30, highlight = selected.authors) p <- p + guides(fill = "none") p <- p + ylab("Title count") p <- p + scale_fill_manual(values = c("darkgray", "red")) print(p)
theme_set(theme_bw(20)) dfa <- dfs[, c("author.name", "author.unique", "author.birth", "author.death")] dfa <- dfa[!duplicated(dfa), ] #dfa <- dfa[match(names(a), dfa$author.name),] dfa <- arrange(dfa, author.birth) # Order authors by birth year dfa$author.name <- factor(dfa$author.name, levels = dfa$author.name) dfa$index <- sample(factor(1:nrow(dfa))) dfa$fill <- rep("black", nrow(dfa)) dfa$fill[dfa$author.unique %in% selected.authors] <- "red" p <- ggplot(dfa) p <- p + geom_segment(aes(y = author.name, yend = author.name, x = author.birth, xend = author.death, color = fill), size = 2) p <- p + theme(axis.text.y = element_text(size = 14)) p <- p + xlab("Author life span (year)") + ylab("") p <- p + guides(color = FALSE) p <- p + scale_color_manual(values = c("darkgray", "red")) print(p)
theme_set(theme_bw(20)) dfs <- df %>% filter(author.unique %in% selected.authors) %>% group_by(author.unique, publication.year) %>% tally() %>% arrange(publication.year) theme_set(theme_bw(20)) # Barplot version p2 <- ggplot(dfs, aes(x = publication.year, y = n, fill = author.unique)) + geom_bar(stat = "identity", position = "stack") + xlab("Publication Year") + ylab("Title Count") p2
library(tidyr) dfs <- df dfs$gatherings <- dfs$document.dimension.gatherings.estimated dfs$gatherings <- gsub("2long", "folio", dfs$gatherings) dfs$gatherings <- gsub("2to", "folio", dfs$gatherings) dfs$gatherings <- gsub("2small", "folio", dfs$gatherings) dfs$gatherings <- gsub("8to", "octavo", dfs$gatherings) top <- names(rev(sort(table(df$author.unique))))[1:20] top <- c(top, "hume, david (1560-1630)") top <- setdiff(top, "caesar, julius (-100- -44)") dfs <- dfs %>% filter(author.unique %in% top & gatherings %in% c("folio", "octavo")) %>% group_by(author.unique, gatherings) %>% tally() %>% spread(gatherings, n) dfs$highlight <- dfs$author.unique %in% c(selected.authors, "burke, edmund (1729-1797)") dfs[is.na(dfs)] <- 0 dfs[dfs$author.unique == "hume, david (1560-1630)","highlight"] <- FALSE dfs$size <- 2 + 1*as.numeric(dfs$highlight) theme_set(theme_bw(20)) p <- ggplot(dfs, aes(x = folio, y = octavo)) + geom_text(aes(label = author.unique, color = highlight, size = size)) + xlab("Folio") + ylab("Octavo") + guides(color = "none", size = "none") + scale_color_manual(values = c("darkgray", "red")) + xlim(-12,max(dfs$folio) + 8) + scale_size(range = c(4,7)) print(p) theme_set(theme_bw(11)) p <- ggplot(dfs, aes(x = publication.year, y = n)) + geom_bar(stat = "identity") + facet_grid(author.unique ~ .) print(p)
theme_set(theme_bw(20)) dfs <- df %>% group_by(author.unique) %>% summarize(titles = n(), paper = sum(paper, na.rm = T)) dfs$highlight <- rep("darkgray", nrow(dfs)) dfs$highlight[dfs$author.unique %in% selected.authors] <- "red" dfs <- dfs %>% filter(as.character(author.unique) %in% top) p <- ggplot(dfs, aes(x = titles, y = paper, color = highlight)) + geom_text(aes(label = author.unique)) + scale_color_manual(values = c("darkgray", "red")) + #scale_x_log10() + scale_y_log10() + guides(color = "none") + xlim(c(-35, 180)) + xlab("Title Count") + ylab("Paper consumption") print(p)
theme_set(theme_bw(20)) dfs <- df %>% filter(author.gender == "female") dfs <- dfs[-c(grep("jean", dfs$author.unique), grep("pierre", dfs$author.unique)),] p <- top_plot(dfs, "author.unique", ntop = 20) p <- p + scale_fill_manual(values = "black") p <- p + guides(fill = "none") p <- p + ylab("Title Count") print(p)
theme_set(theme_bw(20)) p <- top_plot(df, "publication.place", ntop = 20) p <- p + scale_fill_manual(values = "black") p <- p + guides(fill = "none") p <- p + ylab("Title Count") p <- p + scale_y_log10() print(p)
dfs <- df %>% filter(publication.country == "Scotland") p <- top_plot(dfs, "author.unique", ntop = 20, highlight = selected.authors) p <- p + scale_fill_manual(values = c("darkgray", "red")) p <- p + ylab("Title count") p <- p + guides(fill = "none") print(p)
theme_set(theme_bw(20)) top <- names(rev(sort(table(df$publication.place))))[1:50] #dfs <- df %>% filter(publication.place %in% top) %>% group_by(publication.place) %>% summarize(titles = n(), paper = sum(paper, na.rm = T)) #dfs <- dfs %>% filter(as.character(author.unique) %in% top) #p <- ggplot(dfs, aes(x = log10(titles), y = log10(paper), color = country)) + # geom_text(aes(label = publication.place, color = country), size = 5) theme_set(theme_bw(20)) top <- names(rev(sort(table(df$publication.place))))[1:50] dfs <- df %>% filter(publication.place %in% top) %>% group_by(publication.place) %>% summarize(titles = n(), paper = sum(paper, na.rm = T)) dfs$country <- get_country(dfs$publication.place)$country #dfs <- dfs %>% filter(as.character(author.unique) %in% top) p <- ggplot(dfs, aes(x = log10(titles), y = log10(paper))) + geom_text(aes(label = publication.place, color = country), size = 5) + xlim(c(1.2, log10(max(na.omit(dfs$titles[!is.infinite(dfs$titles)]))))) xlab("Title Count") + ylab("Paper consumption") print(p)
dfs <- df %>% filter(subject.topic == "Broadsides") %>% group_by(publication.year) %>% tally() %>% arrange(publication.year) theme_set(theme_bw(20)) p <- ggplot(dfs, aes(x = publication.year, y = n)) + geom_point() + geom_line() + xlab("Publication Year") + ylab("Title Count") print(p)
library(sorvi) dfs <- df %>% group_by(publication.year) %>% summarize(titles = n(), paper = sum(paper, na.rm = T)) p1 <- regression_plot(titles ~ publication.year, data = dfs) p1 <- p1 + xlab("Publication Year") + ylab("Title Count") p1 <- p1 + theme(axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15)) p1 <- p1 + theme(axis.title.x = element_text(size = 15), axis.title.y = element_text(size = 15)) print(p1)
p2 <- regression_plot(paper ~ publication.year, data = dfs) + xlab("Publication Year") + ylab("Paper Consumption") p2 <- p2 + theme(axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15)) p2 <- p2 + theme(axis.title.x = element_text(size = 15), axis.title.y = element_text(size = 15)) print(p2)
theme_set(theme_bw(20)) library(sorvi) dfs <- df %>% group_by(publication.year) %>% summarize(paper.per.doc = mean(paper, na.rm = T)) p <- ggplot(dfs, aes(y = paper.per.doc, x = publication.year)) + geom_line() + geom_point() + #geom_bar(stat = "identity") + xlab("Publication Year") + ylab("Paper per document") print(p)
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