Data Mining with R programming
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R is a widely used programming language and software environment for statistical analysis and graphics. It was first created in 1993 at the University of Auckland as an implementation of the S language. R compiles and runs on Windows, Mac, and UNIX systems and includes packages for text mining, such as tm and SnowballC, which are used to preprocess text through steps like lowering case, removing stopwords and numbers, and stemming words. It also supports creating word clouds and sentiment analysis visualizations.
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Data Mining with R programming
- 2. What is R? R is worlds most widely used statistics programming language . R is a programming language and software environment for Statistical analysis. Graphics representation and reporting . R provides a suite of operators for calculations on arrays, lists, vectors and matrices.
- 3. History R is a programming language it was an implementation over S language. R was first designed by Ross Ihaka and Robert Gentleman at the University of Auckland in 1993 It was stable released on October 31st 2014 the four months ago, by R Development Core Team Under GNU General Public License
- 4. Introduction R is a programming language and software environment for statistical computing and graphics The R language is widely used among statisticians software and data analysis It compiles and runs on a wide variety of UNIX platforms, Windows and Mac OS. R can be downloaded and installed from CRAN website, CRAN stands for Comprehensive R Archive Network
- 5. R - Data Types Primitive (or atomic) data types in R are: Numeric (integer, double, complex) Character Logical Function
- 6. Text Mining with R R is an open source language and environment for statistical computing and graphics. It includes packages like tm, SnowballC, ggplot2 and wordcloud, which are used to carry out the earlier-mentioned steps in text processing. The first prerequisite is that Rand R Studio need to be installed on your machine. R is an open source language and environment for statistical computing and graphics. It includes packages like tm, SnowballC, ggplot2 and wordcloud, which are used to carry out the earlier-mentioned steps in text processing. The first prerequisite is that Rand R Studio need to be installed on your machine.
- 7. Packages Used in Text Mining RSQLite, SQLite Interface for R tm, framework for text mining applications SnowballC, text stemming library Wordloud, for making wordCloud visualizations Syuzhet, text sentiment analysis
- 9. Reading SQLite data in R Docs <- Corpus(docs,VectorSource(docs$comments)) # Get all the emails sent by Hillary Comm <- read.csv(comments.csv, header = TRUE) emailRaw <- paste(emailHillary$EmailBody, collapse=" // ")
- 10. Cleaning Text in R Install.packages(tm) Install.packages(NLP) Load text mining package - library(tm) docs <- Corpus(VerctorSum(emailRaw)) Corpus it is a collection of text documents
- 11. Processing text in R docs <- tm_map(docs, content_transformer(tolower)) It makes all the words to lower cases. docs <- tm_map(docs, removeNumbers) - It removes numbers docs <- tm_map(docs, removeWords, stopWords(english)) It removes stop words like the, is, of docs <- tm_map(docs, removePunctuation) It removes Punctuation docs <- tm_map(docs, stripWhiteSpace) It removes extra White Spaces
- 12. SnowballC to Stem Text #Text stemming (reduces words to their root form) library("SnowballC") docs <- tm_map(docs, stemDocument) # Remove additional stopwords docs <- tm_map(docs, removeWords, c("clintonemailcom", "stategov", "hrod"))
- 13. SnowballC to Stem Text dtm <- TermDocumentMatrix(docs) m <- as.matrix(dtm) v <- sort(rowSums(m),decreasing=TRUE) d <- data.frame(word = names(v),freq=v) head(d, 10)
- 14. Some picture Visualizations #Wordcloud Uses two libraries libraries wordcloud and RcolorBrewer #Sentiment Analysis Uses library - syuzhet
- 23. k
Editor's Notes
- #3: Old programming No multithreading Data loaded directly into memory limits fuctionlaity for larger datasets Sandboxsubsample data Microsoft working on multicore r h2o