stratified splitting the data

Question

I have a large data set and like to fit different logistic regression for each City, one of the column in my data. The following 70/30 split works without considering City group.

indexes <- sample(1:nrow(data), size = 0.7*nrow(data))

train <- data[indexes,]
test <- data[-indexes,]

But this does not guarantee the 70/30 split for each city.

lets say that I have City A and City B, where City A has 100 rows, and City B has 900 rows, totaling 1000 rows. Splitting the data with above code will give me 700 rows for train and 300 for test data, but it does not guarantee that i will have 70 rows for City A, and 630 rows for City B in the train data. How do i do that?

Once i have the training data split-ed to 70/30 fashion for each city,i will run logistic regression for each city ( I know how to do this once i have the train data)

Answer 1

Try createDataPartition from caret package. Its document states: By default, createDataPartition does a stratified random split of the data.

library(caret)
train.index <- createDataPartition(Data$Class, p = .7, list = FALSE)
train <- Data[ train.index,]
test  <- Data[-train.index,]

it can also be used for stratified K-fold like:

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 3,
                     ...)
# when calling train, pass this train control
train(...,
      trControl = ctrl,
      ...)

check out caret document for more details