Categories: Big Data

Learn R – How to Fix Read.Table Command Reading Lesser Rows

This article represents the problem statement related with read.table reading fewer or incorrect or lesser number of lines or rows when reading a text file having multiple columns, and the solution to the same. This is going to be a shorter blog. But since it solved a problem on which I spent some time, I chose to write about the same. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.

Problem Statement: Reading Fewer Lines with read.table Command

I have been learning the naive bayes classification. I downloaded this SMS collection data. I went ahead and tried to load the data using following command. And, it listed around 1630 rows, although there were 5574 rows.

messages <- read.table( file.choose(), sep="\t", stringsAsFactors=FALSE)

I check with commands such as dim(messages) and it gave me 1630 messages with 2 columns. This is lesser (and thus, incorrect) than what existed in the document.

Solution to getting exact number of rows

After investigation, I found that the messages consisted of single/double quotes and this needed to be disabled for read.table to read correct number of rows. I did the same with following command and it worked pretty well. Note the usage quote=” parameter.

messages <- read.table( file.choose(), sep="\t", stringsAsFactors=FALSE, quote='')

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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.