Partitioning data set in r based on multiple classes of observations
I'm trying to partition a data set that I have in R, 2/3 for training and 1/3 for testing. I have one classification variable, and seven numerical variables. Each observation is classified as either A, B, C, or D.
For simplicity's sake, let's say that the classification variable, cl, is A for the first 100 observations, B for observations 101 to 200, C till 300, and D till 400. I'm trying to get a partition that has 2/3 of the observations for each of A, B, C, and D (as opposed to simply getting 2/3 of the observations for the entire data set since it will likely not have equal amounts of each classification).
When I try to sample from a subset of the data, such as sample(subset(data, cl=='A')), the columns are reordered instead of the rows.
To summarize, my goal is to have 67 random observations from each of A, B, C, and D as my training data, and store the remaining 33 observations for each of A, B, C, and D as testing data. I have found a very similar question to mine, but it did not factor in multiple variables.
Answer
There is actually a nice package caret for dealing with machine learning problems and it contains a function createDataPartition() that pretty much does this sampling 2/3rds from each level of a supplied factor:
#2/3rds for training
library(caret)
inTrain = createDataPartition(df$yourFactor, p = 2/3, list = FALSE)
dfTrain=df[inTrain,]
dfTest=df[-inTrain,]