Stratified Sampling a Dataset and Averaging a Variable within the Train Dataset - r

Question

I'm currently trying to do a stratified split in R to create train and test datasets.
A problem posed to me is the following
split the data into a train and test sample such that 70% of the data
is in the train sample. To ensure a similar distribution of price
across the train and test samples, use createDataPartition from the
caret package. Set groups to 100 and use a seed of 1031. What is the
average house price in the train sample?
The dataset is a set of houses with prices (along with other data points)
For some reason, when I run the following code, the output I get is labeled as incorrect in the practice problem simulator. Can anyone spot an issue with my code? Any help is much appreciated since I'm trying to avoid learning this language incorrectly.
dput(head(houses))
library(ISLR); library(caret); library(caTools)
options(scipen=999)
set.seed(1031)
#STRATIFIED RANDOM SAMPLING with groups of 100, stratefied on price, 70% in train
split = createDataPartition(y = houses$price,p = 0.7,list = F, groups = 100)
train = houses[split,]
test = houses[-split,]
nrow(train)
nrow(test)
nrow(houses)
mean(train$price)
mean(test$price)
Output
> dput(head(houses))
structure(list(id = c(7129300520, 6414100192, 5631500400, 2487200875,
1954400510, 7237550310), price = c(221900, 538000, 180000, 604000,
510000, 1225000), bedrooms = c(3, 3, 2, 4, 3, 4), bathrooms = c(1,
2.25, 1, 3, 2, 4.5), sqft_living = c(1180, 2570, 770, 1960, 1680,
5420), sqft_lot = c(5650, 7242, 10000, 5000, 8080, 101930), floors = c(1,
2, 1, 1, 1, 1), waterfront = c(0, 0, 0, 0, 0, 0), view = c(0,
0, 0, 0, 0, 0), condition = c(3, 3, 3, 5, 3, 3), grade = c(7,
7, 6, 7, 8, 11), sqft_above = c(1180, 2170, 770, 1050, 1680,
3890), sqft_basement = c(0, 400, 0, 910, 0, 1530), yr_built = c(1955,
1951, 1933, 1965, 1987, 2001), yr_renovated = c(0, 1991, 0, 0,
0, 0), age = c(59, 63, 82, 49, 28, 13)), row.names = c(NA, -6L
), class = c("tbl_df", "tbl", "data.frame"))
>
> library(ISLR); library(caret); library(caTools)
> options(scipen=999)
>
> set.seed(1031)
> #STRATIFIED RANDOM SAMPLING with groups of 100, stratefied on price, 70% in train
> split = createDataPartition(y = houses$price,p = 0.7,list = F, groups = 100)
>
> train = houses[split,]
> test = houses[-split,]
>
> nrow(train)
[1] 15172
> nrow(test)
[1] 6441
> nrow(houses)
[1] 21613
>
> mean(train$price)
[1] 540674.2
> mean(test$price)
[1] 538707.6

Answer 1

I try to reproduce it manually using sample_frac form dplyr package and cut2 function from Hmisc package. The results are almost the same - still not same.
It looks like there might be a problem with pseudo numbers generator or with some rounding.
In my opinion your code looks to be a correct one.
Is it possible that in previous steps you should remove some outliers or pre-process dataset in any way.
library(caret)
options(scipen=999)
library(dplyr)
library(ggplot2) # to use diamonds dataset
library(Hmisc)
diamonds$index = 1:nrow(diamonds)
set.seed(1031)
# I use diamonds dataset from ggplot2 package
# g parameter (in cut2) - number of quantile groups
split = diamonds %>%
group_by(cut2(diamonds$price, g= 100)) %>%
sample_frac(0.7) %>%
pull(index)
train = diamonds[split,]
test = diamonds[-split,]
> mean(train$price)
[1] 3932.75
> mean(test$price)
[1] 3932.917
set.seed(1031)
#STRATIFIED RANDOM SAMPLING with groups of 100, stratefied on price, 70% in train
split = createDataPartition(y = diamonds$price,p = 0.7,list = T, groups = 100)
train = diamonds[split$Resample1,]
test = diamonds[-split$Resample1,]
> mean(train$price)
[1] 3932.897
> mean(test$price)
[1] 3932.572
This sampling procedure should result in mean that approximate to a population one.