Extracting Country Name from Author Affiliations

Question

I am currently exploring the possibility of extracting country name from Author Affiliations (PubMed Articles) my sample data looks like:

Mechanical and Production Engineering Department, National University of Singapore.

Cancer Research Campaign Mammalian Cell DNA Repair Group, Department of Zoology, Cambridge, U.K.

Cancer Research Campaign Mammalian Cell DNA Repair Group, Department of Zoology, Cambridge, UK.

Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 46285.

Initially I tried to remove punctuations and split the vector into words and then compared it with a list of country names from Wikipedia but I am not successful at this.

Can anyone please suggest me a better way of doing it? I would prefer the solution in R as I have to do further analysis and generate graphics in R.

Answer 1

Here is a simple solution that might get you started some of the way. It makes use of a database containing city and country data in the maps package. If you can get hold of a better database, it should be simple to modify the code.

library(maps)
library(plyr)

# Load data from package maps
data(world.cities)

# Create test data
aa <- c(
    "Mechanical and Production Engineering Department, National University of Singapore.",
    "Cancer Research Campaign Mammalian Cell DNA Repair Group, Department of Zoology, Cambridge, U.K.",
    "Cancer Research Campaign Mammalian Cell DNA Repair Group, Department of Zoology, Cambridge, UK.",
    "Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 46285."
)

# Remove punctuation from data
caa <- gsub(aa, "[[:punct:]]", "")    ### *Edit*

# Split data at word boundaries
saa <- strsplit(caa, " ")

# Match on cities in world.cities
# Assumes that if multiple matches, the last takes precedence, i.e. max()
llply(saa, function(x)x[max(which(x %in% world.cities$name))])

# Match on country in world.countries
llply(saa, function(x)x[which(x %in% world.cities$country.etc)])

This is the result for cities:

[[1]]
[1] "Singapore"

[[2]]
[1] "Cambridge"

[[3]]
[1] "Cambridge"

[[4]]
[1] "Indianapolis"

And the result for countries:

[[1]]
[1] "Singapore"

[[2]]
[1] "UK"

[[3]]
[1] "UK"

[[4]]
character(0)

With a bit of data cleanup you may be able to do something with this.