Created
June 14, 2017 09:58
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| library(dplyr) | |
| library(tidyr) | |
| library(ggplot2) | |
| library(gridExtra) | |
| library(grid) | |
| data20162017 <- read.csv("toshiny_altm_2016_2017.csv", stringsAsFactors = FALSE) | |
| data20162017 <- data20162017 %>% | |
| mutate(gap_cites = Citations2017 - Citations, | |
| gap_tweets = Twitter2017 - Twitter, | |
| gap_mend = Mendeley2017 - Mendeley) | |
| data20162017_gathered <- gather(data20162017, metrics, value, Citations, Citations2017, Mendeley, Mendeley2017, Twitter, Twitter2017) | |
| data20162017_gathered <- data20162017_gathered %>% | |
| mutate(Date = ifelse(grepl('2017', metrics), | |
| format(as.Date("2017-05-30", format="%Y-%m-%d"),"%Y-%m-%d"), | |
| format(as.Date("2016-05-13", format="%Y-%m-%d"),"%Y-%m-%d"))) | |
| data20162017_gathered$metrics <- gsub("([^0-9]*)[0-9]+","\\1",data20162017_gathered$metrics) | |
| data20162017_gathered$Date <- as.Date(data20162017_gathered$Date, origin="1970-01-01") | |
| data20162017_gathered$value <- as.numeric(data20162017_gathered$value) | |
| data20162017_gathered$DOI <- gsub("/", "/\n", data20162017_gathered$DOI) | |
| data20162017_gathered$value[is.na(data20162017_gathered$value)] <- 0 | |
| # https://blog.dominodatalab.com/visualizing-homeownership-in-the-us-using-small-multiples-and-r/ | |
| opts <- theme( | |
| panel.background = element_rect(fill="white"), | |
| panel.border = element_rect(colour="black", fill=NA), | |
| axis.line = element_blank(), | |
| axis.ticks = element_blank(), | |
| panel.grid.major = element_blank(), | |
| panel.grid.minor = element_blank(), | |
| axis.text = element_text(colour="gray25", size=15, angle=90, hjust=1), | |
| axis.title = element_blank(), | |
| text = element_text(size=12), | |
| legend.key = element_blank(), | |
| legend.position = "none", | |
| legend.background = element_blank(), | |
| plot.title = element_text(size = 45)) | |
| vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y) | |
| doschool <- function(data = NULL, sc = NULL, m = NULL) { | |
| m_name <- if(m=="gap_cites") "citations" else if(m=="gap_tweets") "tweets" else "Mendeley readers" | |
| message("Now printing ", sc, " sorted by diff in ", m_name) | |
| flush.console() | |
| s <- data %>% | |
| filter(School == sc) %>% | |
| arrange_(.dots = paste0("desc(", m, ")")) | |
| dois <- unique(s$DOI) | |
| if (length(dois)<=50) { | |
| dois <- dois | |
| } else { | |
| dois <- dois[1:100] | |
| } | |
| grid.newpage() | |
| if ( s[1, "School"]=="ARTS" ) { | |
| png(file=paste0("altm_", m, "_", sc, ".png"), height = 1500, width = 3000) | |
| pushViewport(viewport(layout = grid.layout(7, 10, heights=c(.3,.1,rep(1,5)), widths=c(1.1, rep(1,9))))) | |
| } else { | |
| png(file=paste0("altm_", m, "_", sc, ".png"), height = 3500, width = 4000) | |
| pushViewport(viewport(layout = grid.layout(12, 10, heights=c(.3,.1,rep(1,10)), widths=c(1.1, rep(1,9))))) | |
| } | |
| grid.text("WoS citations, Mendeley readers, and tweets 2016-2017", | |
| gp=gpar(fontsize=50, col="gray40"), | |
| vp = viewport(layout.pos.row = 1, | |
| layout.pos.col = 1:10)) | |
| grid.text(paste0("Aalto University, School of ", sc, ". Max 100 items published 2013-2015, sorted by changes in ", m_name, " between 2016-05-13 and 2017-06-01."), | |
| gp=gpar(fontsize=30, col="gray40"), | |
| vp = viewport(layout.pos.row = 2, | |
| layout.pos.col = 1:10)) | |
| for (i in 1:length(dois)) | |
| { | |
| datap <- subset(s, DOI==dois[i]) | |
| p <- ggplot(datap, aes(Date, value))+ | |
| geom_rect(data = datap, | |
| aes(fill = factor(metrics)), | |
| xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, alpha = NA)+ | |
| scale_fill_brewer(palette="Set2")+ | |
| geom_line(size=1.5)+ | |
| facet_wrap(DOI ~ metrics)+ | |
| {if ( datap[1,"School"]=="ARTS" ) { scale_y_continuous(expand = c(0, 0), limits = c(0,190)) } else { scale_y_log10(limits = c(1,1e3)) }}+ | |
| scale_x_date(date_breaks = "1 year", date_labels = "%Y")+ | |
| opts | |
| if (i%%10!=1) p <- p+theme(axis.text.y = element_blank()) | |
| if (datap[1,"School"]=="ARTS") { | |
| if (i<=40) p <- p+theme(axis.text.x = element_blank()) | |
| } else { | |
| if (i<=90) p <- p+theme(axis.text.x = element_blank()) | |
| } | |
| print(p, vp = vplayout(floor((i-1)/10)+3, i%%10+(i%%10==0)*10)) | |
| } | |
| dev.off() | |
| } | |
| metrics <- c("gap_cites", "gap_tweets", "gap_mend") | |
| schools <- sort(unique(data_gathered$School)) | |
| for (i in 1:length(schools)) { | |
| for (j in 1:length(metrics)) { | |
| doschool(data20162017_gathered, schools[i], metrics[j]) | |
| } | |
| } | |
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