Parsing RTF files into R? - r

Question

Couldn't find much support for this for R. I'm trying to read a number of RTF files into R to construct a data frame, but I'm struggling to find a good way to parse the RTF file and ignore the structure/formatting of the file. There are really only two lines of text I want to pull from each file -- but it's nested within the structure of the file.
I've pasted a sample RTF file below. The two strings I'd like to capture are:
"Buy a 26 Inch LCD-TV Today or a 32 Inch Next Month? Modeling Purchases of High-tech Durable Products"
"The technology level [...] and managerial implications." (the full paragraph)
Any thoughts on how to efficiently parse this? I think regular expressions might help me, but I'm struggling to form the right expression to get the job done.
{\rtf1\ansi\ansicpg1252\cocoartf1265
{\fonttbl\f0\fswiss\fcharset0 ArialMT;\f1\froman\fcharset0 Times-Roman;}
{\colortbl;\red255\green255\blue255;\red0\green0\blue0;\red109\green109\blue109;}
\margl1440\margr1440\vieww10800\viewh8400\viewkind0
\deftab720
\itap1\trowd \taflags0 \trgaph108\trleft-108 \trbrdrt\brdrnil \trbrdrl\brdrnil \trbrdrt\brdrnil \trbrdrr\brdrnil
\clvertalt \clshdrawnil \clwWidth15680\clftsWidth3 \clbrdrt\brdrnil \clbrdrl\brdrnil \clbrdrb\brdrnil \clbrdrr\brdrnil \clpadl0 \clpadr0 \gaph\cellx8640
\itap2\trowd \taflags0 \trgaph108\trleft-108 \trbrdrt\brdrnil \trbrdrl\brdrnil \trbrdrt\brdrnil \trbrdrr\brdrnil
\clmgf \clvertalt \clshdrawnil \clwWidth14840\clftsWidth3 \clbrdrt\brdrnil \clbrdrl\brdrnil \clbrdrb\brdrnil \clbrdrr\brdrnil \clpadl0 \clpadr0 \gaph\cellx4320
\clmrg \clvertalt \clshdrawnil \clwWidth14840\clftsWidth3 \clbrdrt\brdrnil \clbrdrl\brdrnil \clbrdrb\brdrnil \clbrdrr\brdrnil \clpadl0 \clpadr0 \gaph\cellx8640
\pard\intbl\itap2\pardeftab720
\f0\b\fs26 \cf0 Buy a 26 Inch LCD-TV Today or a 32 Inch Next Month? Modeling Purchases of High-tech Durable Products\nestcell
\pard\intbl\itap2\nestcell \lastrow\nestrow
\pard\intbl\itap1\pardeftab720
\f1\b0\fs24 \cf0 \
\pard\intbl\itap1\pardeftab720
\f0\fs26 \cf0 The technology level of new high-tech durable products, such as digital cameras and LCD-TVs, continues to go up, while prices continue to go down. Consumers may anticipate these trends. In particular, a consumer faces several options. The first is to buy the current level of technology at the current price. The second is not to buy and stick with the currently owned (old) level of technology. Hence, the consumer postpones the purchase and later on buys the same level of technology at a lower price, or better technology at the same price. We develop a new model to describe consumers\'92 decisions with respect to buying these products. Our model is built on the theory of consumer expectations of price and the well-known utility maximizing framework. Since not every consumer responds the same, we allow for observed and unobserved consumer heterogeneity. We calibrate our model on a panel of several thousand consumers. We have information on the currently owned technology and on purchases in several categories of high-tech durables. Our model provides new insights in these product markets and managerial implications.\cell \lastrow\row
\pard\pardeftab720
\f1\fs24 \cf0 \
}

Answer 1

1) A simple way if you are on Windows is to read it in using WordPad or Word and then save it as a plain text document.
2) Alternately, to parse it directly in R, read in the rtf file, find lines with the given pattern, pat producing g. Then replace any \\' strings with single quotes producing noq. Finally remove pat and any trailing junk. This works on the sample but you might need to revise the patterns if there are additional embedded \\ strings other than the \\' which we already handle:
Lines <- readLines("myfile.rtf")
pat <- "^\\\\f0.*\\\\cf0 "
g <- grep(pat, Lines, value = TRUE)
noq <- gsub("\\\\'", "'", g)
sub("\\\\.*", "", sub(pat, "", noq))
For the indicated file this is the output:
[1] "Buy a 26 Inch LCD-TV Today or a 32 Inch Next Month? Modeling Purchases of High-tech Durable Products"
[2] "The technology level of new high-tech durable products, such as digital cameras and LCD-TVs, continues to go up, while prices continue to go down. Consumers may anticipate these trends. In particular, a consumer faces several options. The first is to buy the current level of technology at the current price. The second is not to buy and stick with the currently owned (old) level of technology. Hence, the consumer postpones the purchase and later on buys the same level of technology at a lower price, or better technology at the same price. We develop a new model to describe consumers'92 decisions with respect to buying these products. Our model is built on the theory of consumer expectations of price and the well-known utility maximizing framework. Since not every consumer responds the same, we allow for observed and unobserved consumer heterogeneity. We calibrate our model on a panel of several thousand consumers. We have information on the currently owned technology and on purchases in several categories of high-tech durables. Our model provides new insights in these product markets and managerial implications."
Revised several times. Added Wordpad/Word solution.