Articles with Events from PLOS Biology

# code for figure 4: bar plot for Article-level metrics for PLOS Biology

# Load required libraries
library(reshape2)

# load May 20, 2013 ALM report
alm <- read.csv("../data/alm_report_plos_biology_2013-05-20.csv", stringsAsFactors=FALSE, na.strings=c(0, "0"))

# only look at research articles
alm <- subset(alm, alm$article_type == "Research Article")

# make sure columns are in the right format
alm$counter_html <- as.numeric(alm$counter_html)
alm$mendeley <- as.numeric(alm$mendeley)

# options
plos.color <- "#1ebd21"
plos.colors <- c("#a17f78","#ad9a27","#ad9a27","#ad9a27","#ad9a27","#ad9a27","#dcebdd","#dcebdd","#789aa1","#789aa1","#789aa1","#304345","#304345")

# use subset of columns
alm <- subset(alm, select=c("f1000","wikipedia","researchblogging","comments","facebook","twitter","citeulike","mendeley","pubmed","crossref","scopus","pmc_html","counter_html"))

# calculate percentage of values that are not missing (i.e. have a count of at least 1)
colSums <- colSums(!is.na(alm)) * 100 / length(alm$counter_html)
exactSums <- sum(as.numeric(alm$pmc_html),na.rm =TRUE)

# plot the chart
opar <- par(mar=c(0.1,7.25,0.1,0.1)+0.1,omi=c(0.1,0.25,0.1,0.1),col.main=plos.color)

plos.names <- c("F1000Prime","Wikipedia","Research Blogging","PLOS Comments", "Facebook", "Twitter","CiteULike","Mendeley","PubMed Citations", "CrossRef", "Scopus", "PMC HTML Views", "PLOS HTML Views")
y <- barplot(colSums,horiz=TRUE,col=plos.colors, border = NA, xlab=plos.names, xlim=c(0,120), axes=FALSE, names.arg=plos.names,las=1, adj=0)
text(colSums+6,y,labels=sprintf("%1.0f%%", colSums))

par(opar)