To do this required me to download citation data and do some analysis. Downloading citation data is not difficult but it does take up a bit of internet time. You don't need to do it yourself to appreciate the graph that I have made or the list of papers that it generated.
Here is the graph:
Here is the script to draw this graph:
# This data file has a list of the PubMed IDs, the year and the citation data
data <- read.csv("http://science2therapy.com/data/cllCitation2010to2014_20150722.csv", header=T)
cit <- data$cit
# not very useful but good practice to plot the data first...
# not very useful in ggplot either.
p <- ggplot(data=data, # specify the data frame with data
aes(x=cit)) + # specify the x and y for the graph
geom_bar(binwidth = 10) # it's a bar plot
p # show the plot
# so lots of the publications with relatively few citations.
# Do some subsetting to identify highly cited papers.
# calculate the mean number of citations
mean.cit <- mean(data$cit) # 4.1 for this data set.
# data frame of publications with no citations
newdata.zero <- subset(data, cit == 0)
# data frame of publications with one citation
newdata.one <- subset(data, cit == 1)
# make a data frame of publications with more than one citations upto the mean
newdata.greater1 <- subset(data, cit > 1)
newdata.mean <- subset(newdata.greater1, cit < mean.cit)
# make a data frame of publications with more than the mean citations
# up to the mean squared
newdata.greatermean <- subset(data, cit > mean.cit)
newdata.meanSq <- subset(newdata.greatermean, cit < (mean.cit^2))
# make a data frame of publications with more than the mean squared citations
# up to the mean cubed
newdata.greatermeanSq <- subset(data, cit > (mean.cit^2))
newdata.SqtoCube <- subset(newdata.greatermeanSq, cit < (mean.cit^3))
# make a data frame of publications with more than the mean cubed citations
newdata.greaterCube <- subset(data, cit > (mean.cit^3))
# assemble these numbers into a vector
count<-c(nrow(newdata.zero), nrow(newdata.one), nrow(newdata.mean),
nrow(newdata.meanSq), nrow(newdata.SqtoCube), nrow(newdata.greaterCube))
# simple barplot
# create a list of labels
# assemble a new data frame to plot with ggplot
df <- as.data.frame(count)
df$label <- lab
df$labfac <- factor(df$label, as.character(df$label))
# do a nice histogram of citation frequency in ggplot
p <- ggplot(data=df, aes(y=count)) +
geom_bar(aes(x=labfac), data=df, stat="identity") +
xlab("Number of citations") + # label x-axis
ylab("Number of Papers") + # label y-axis
ggtitle("Chronic Lymphocytic Leukemia Papers published 2010 to 2014") + # add a title
theme_bw() + # a simple theme
expand_limits(y=c(0,2000)) + # customise the y-axis
theme(axis.title.y = element_text(size = 14 )) +
theme(axis.title.x = element_text(size = 14 )) +
theme(axis.text = element_text(size = 12))
p #show us the plot
Thoughts on the citation analysis
The average number of citations per paper was just over 4.
Only 24 papers were cited more than 64 times.
Here is a list of the 5 most highly cited papers from 2010 to 2014:
- Porter, et al 2011 N Engl J Med “Chimeric antigen receptor-modified T cells in chronic lymphoid leukemia.” Cited: 414 times
- Stephens et al 2011 Cell “Massive genomic rearrangement acquired in a single catastrophic event during cancer development.” Cited: 333 times
- Kalos et al 2011 Sci Transl Med “T cells with chimeric antigen receptors have potent antitumor effects and can establish memory in patients with advanced leukemia.” Cited: 287 times
- Puente et al 2011 Nature “Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia.” Cited: 228 times
- Grupp et al 2013 N Engl J Med “Chimeric antigen receptor-modified T cells for acute lymphoid leukemia.” Cited: 205 times
Full list of the 24 papers is available as a PDF here.