sketch of citation analysis

citation-analysis-sketch.R

# sources:
# http://www.jgoodwin.net/?p=1223
# http://orgtheory.wordpress.com/2012/05/16/the-fragile-network-of-econ-soc-readings/
# http://nealcaren.web.unc.edu/a-sociology-citation-network/
# http://kieranhealy.org/blog/archives/2014/11/15/top-ten-by-decade/
# http://www.jgoodwin.net/lit-cites.png


###########################################################################
# This first section scrapes content from the Web of Science webpage. It takes
# a little bit of setting up and clicking around, then the loop takes care of
# the time-consuming bit. I used Ubuntu 14.04 to do this (not on a VM)

vignette('RSelenium-basics')
# setup broswer and selenium
library(devtools)
install_github("ropensci/rselenium")
library(RSelenium)
checkForServer()
startServer()
remDr <- remoteDriver()
remDr$open()
# go to http://apps.webofknowledge.com.offcampus.lib.washington.edu/
# refine search by journal... perhaps arch?eolog* in 'topic'
# then: 'Research Areas' -> archaeology -> refine
# then: 'Document types' -> article -> refine
# then: 'Source title' -> choose your favourite journals -> refine
# must have <10k results to enable citation data
# click 'create citation report' tab at the top
# do the first page manually to set the 'save file' and 'do this automatically', 
# then let loop do the work after that

# before running the loop, get URL of first page that we already saved,
# and paste in next line
remDr$navigate("http://apps.webofknowledge.com/CitationReport.do?product=UA&search_mode=CitationReport&SID=4CvyYFKm3SC44hNsA2w&page=1&cr_pqid=7&viewType=summary")

# Here's the loop to automate collecting data from the next 600-odd pages...
# Loop to get citation data for each page of results, each iteration will save a txt file
for(i in 1:1000){
  # click on 'save to text file'
  result <- try(
    webElem <- remDr$findElement(using = 'id', value = "select2-chosen-1")
  ); if(class(result) == "try-error") next;
  webElem$clickElement()
  # click on 'send' on pop-up window
  result <- try(
    webElem <- remDr$findElement(using = "css", "span.quickoutput-action")
  ); if(class(result) == "try-error") next;
  webElem$clickElement()
  # refresh the page to get rid of the pop-up
  remDr$refresh()
  # advance to the next page of results
  result <- try(
    webElem <- remDr$findElement(using = 'xpath', value = "(//form[@id='summary_navigation']/table/tbody/tr/td[3]/a/i)[2]")
  ); if(class(result) == "try-error") next;
  webElem$clickElement()
  print(i) 
}

# From here I used a docker container to improve reproducibility and isolation 
# of the analysis. More specifically, I used boot2docker to run docker on Windows
# then ran the rocker/hadleyverse container with a shared folder to my deskstop
# the exact line to enter this container is: 
# docker run -d -p 8787:8787 -v /c/Users/marwick:/home/rstudio/ rocker/hadleyverse
# you'll need to change 'c/Users/marwick' to whatever is equivalent on your machine
# more details: https://github.com/rocker-org/rocker/wiki

# text files collected by this loop can be found here:
# https://drive.google.com/folderview?id=0B87CmPqGXTzldk9QMUlnU0FZYlU&usp=sharing
# there are many duplicates, but the code below will remove them
# copy the folder to your hard drive, and edit the setwd line below
# to match the location of your folder containing the hundreds of text files.

### get all text files into R (move them manually into a folder of their own)
setwd("/home/two/Downloads/WoS")
# get text file names
my_files <- list.files(pattern = ".txt")
# make list object to store all text files in R
my_list <- vector(mode = "list", length = length(my_files))
# loop over file names and read each file into the list
my_list <- lapply(seq(my_files), function(i) read.csv(my_files[i], 
                                                      skip = 4, 
                                                      header = TRUE,                            
                                                      comment.char = " "))
# check to see it worked
my_list[1:5]

## combine list of dataframes into one big dataframe
# use data.table for speed
install_github("rdatatable/data.table")
library(data.table)
my_df <- rbindlist(my_list)
setkey(my_df)
# filter only a few columns to simplify
my_cols <- c('Title', 'Publication.Year', 'Total.Citations', 'Source.Title')
my_df <- my_df[,my_cols, with=FALSE]
# remove duplicates
my_df <- unique(my_df)
# what journals do we have?
unique(my_df$Source.Title)

## make abbreviations for journal names, make article titles all upper case
# get names
long_titles <- as.character(unique(my_df$Source.Title))
# get abbreviations automatically, perhaps not the obvious ones, but it's fast
short_titles <- unname(sapply(long_titles, function(i){
  theletters = strsplit(i,'')[[1]]
  wh = c(1,which(theletters  == ' ') + 1)
  theletters[wh]
  paste(theletters[wh],collapse='') 
}))
# manually disambiguate the journals that now only have 'A' as the short name                         
short_titles[short_titles == "A"] <- c("AMTRY", "ANTQ", "ARCH")
# remove 'NA' so it's not confused with an actual journal
short_titles[short_titles == "NA"] <- ""
# add abbreviations to big table
journals <- data.table(Source.Title = long_titles, 
                       short_title = short_titles)
setkey(journals) # need a key to merge
my_df <- merge(my_df, journals, by = 'Source.Title')
# make article titles all upper case, easier to read
my_df$Title <- toupper(my_df$Title)

                         
## create new column that is 'decade'
# first make a lookup table to get a decade for each individual year
year1 <- 1900:2050
my_seq <- seq(year1[1], year1[length(year1)], by = 10)
indx <- findInterval(year1, my_seq)
ind <- seq(1, length(my_seq), by = 1)
labl1 <- paste(my_seq[ind], my_seq[ind + 1], sep = "-")[-42]
dat1 <- data.table(data.frame(Publication.Year = year1, 
                              decade = labl1[indx], 
                              stringsAsFactors = FALSE))
setkey(dat1, 'Publication.Year')
# merge the decade column onto my_df
my_df <- merge(my_df, dat1, by = 'Publication.Year')

## find the most citated paper by decade of publication

df_top <- my_df[ave(-my_df$Total.Citations, my_df$decade, FUN = rank) <= 10, ] 

# inspecting this df_top table is quite interesting.

# Draw the plot...

######## plotting code from from Jonathan Goodwin ##########
######## http://jgoodwin.net/ [email protected] ########

# format of data: Title, Total.Citations, decade, Source.Title
# THE WRITERS AUDIENCE IS ALWAYS A FICTION,205,1974-1979,PMLA

library(ggplot2)
ws <- df_top

ws <-  ws[order(ws$decade,-ws$Total.Citations),]
ws$Title <- factor(ws$Title, levels = unique(ws$Title)) #to preserve order in plot, maybe there's another way to do this

g <- ggplot(ws, aes(x = Total.Citations, 
                    y = Title, 
                    label = short_title, 
                    group = decade, 
                    colour = short_title))

g <- g + geom_text(size = 4) + 
  facet_grid (decade ~.,
              drop=TRUE,
              scales="free_y") + 
  theme_bw(base_family="Helvetica") +
  theme(axis.text.y=element_text(size=8)) +
  xlab("Number of Web of Science Citations") + ylab("") +
  labs(title="Archaeology's Ten Most-Cited Articles Per Decade (1970-)", size=7) + 
  scale_colour_discrete(name="Journals")

g #adjust sizing, etc.

######## a few other plots #################
library(scales)

# distribution of papers over time (by year)
ggplot(my_df, aes(Publication.Year)) +
  geom_bar() +
  theme_bw(base_family="Helvetica")

# distribution of papers over time (by decade)
ggplot(my_df, aes(decade)) +
  geom_bar() +
  theme_bw(base_family="Helvetica")

# distribution of papers over time (by decade, by journal)
ggplot(my_df, aes(decade, fill = short_title)) +
  geom_bar(binwidth=.5, position="dodge") + 
  theme_bw(base_family="Helvetica")

# distribution of citations within each year
ggplot(my_df, aes(factor(Publication.Year), Total.Citations)) +
  geom_boxplot() + 
  theme_bw(base_family="Helvetica") +  
  theme(axis.text.x  = element_text(angle=90, vjust=0.5, size=16)) +
  scale_y_continuous(trans=log2_trans())

# distribution of citations within each decade
ggplot(my_df, aes(decade, Total.Citations)) +
  geom_boxplot() + 
  scale_y_continuous(trans=log2_trans()) + 
  theme_bw(base_family="Helvetica") 
  
fivenum(my_df$Total.Citations) 
# minimum, lower-hinge, median, upper-hinge, maximum

###################################################################################








# if working manually: 
# scroll to bottom of search results, where 'Output Records' section is
# Step 1: Select records -> 1-500 since 500 is max 
# Step 2: Select content -> Full Record & Cited References (this seems to have changed)
# Step 3: Select destination -> save to plain text

# Once we've got the data, use or adapt this python script
# http://www.unc.edu/~ncaren/cite_network/citenet.py
# and note the endnote here: http://www.jgoodwin.net/?p=1223

# then make network diagrams and calculate typical sna stats...

Hi, I am using this code but for some reason it does not go to the next page and keeps downloading the information from the first page only. some times it works and advances two or three pages but then stops again in one page.

Could you help me to solve this issue?

Hi Ben,
In the loop of automate collecting data from WOS:
is it possible to select "Full record and cited references" before clicking on 'send' ?
Thank you.
Maksym