The purpose of this project is to study which words appear most frequently in street addresses. First we will read in the required libraries.

library(dplyr)
library(knitr)
library(tm)
library(wordcloud2)

Now we will read in the street addresses contained in the Medicare database found at http://tiny.cc/dcf/CMS_ProvidersSimple.rds.

Addresses <- readRDS("Medicare.rds") %>% select(address)
docs <- Corpus(VectorSource(Addresses))

Next we will clean the text.

toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "-")
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")
docs <- tm_map(docs, removeNumbers)

# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)

Now we will extract the word frequency information to a dataframe and then print the 20 most frequent words.

dtm <- TermDocumentMatrix(docs)
d <- as.data.frame(as.matrix(dtm) )
colnames(d) <- c("freq")
d$word<-rownames(d)
rownames(d) <- 1:nrow(d)
d <- d %>% arrange(desc(freq)) %>% select(word, freq)

d %>% head(20) %>% kable(row.names=TRUE)

	word	freq
1	ave	171640
2	blvd	68294
3	ste	29424
4	street	22748
5	main	21937
6	medical	17505
7	pkwy	16500
8	park	15458
9	center	14413
10	road	14291
11	hwy	14037
12	avenue	11216
13	way	11036
14	highway	10934
15	drive	10249
16	hospital	9083
17	state	8044
18	washington	7242
19	university	7102
20	north	7032

Finally we will generate a word cloud with the 200 most common words.

d <- d %>% head(200)

wordcloud2(d, size = 2)