Learn R – How to Get Random Training and Test Data Set

March 26, 2015 by Ajitesh Kumar · Leave a comment
This article represents sample source code which could be used to extract random training and test data set from a data frame using R programming language. The R code below could prove very handy while you are working to create a model using any machine learning algorithm. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.

 

# Read the data from a file; The command below assumes that the working 
# directory has already been set. One could set working directory using 
# setwd() command.
sample_df <- read.csv("glass.data", header=TRUE, stringsAsFactors=FALSE)

# get a vector comprising of all indices starting from 1 and ending with row number
index <- 1:nrow(sample_df)

# Get random indices of size n from index vector; In command below, the 
# size n is determined using trunc(length(index))/3
randindex <- sample(index, trunc(length(index))/3)

# Get the training set consisting of all the items except one represented using 
# randindex
trainset <- sample_df[-randindex,]
# Get the test set represented using random index
testset <- sample_df[randindex,]
Ajitesh Kumar
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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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