# This file contains all the functions of the workflow.
# If needed, you could split it up into multiple files.
# Pick a random subset of n rows from a dataset
random_rows <- function(data, n){
data[sample.int(n = nrow(data), size = n, replace = TRUE), ]
}
# Bootstrapped datasets from mtcars.
simulate <- function(n){
# Pick a random set of cars to bootstrap from the mtcars data.
data <- random_rows(data = mtcars, n = n)
# x is the car's weight, and y is the fuel efficiency.
data.frame(
x = data$wt,
y = data$mpg
)
}
# Try a couple different regression models.
# Is fuel efficiency linearly related to weight?
reg1 <- function(d){
lm(y ~ + x, data = d)
}
# Is fuel efficiency related to the SQUARE of the weight?
reg2 <- function(d){
d$x2 <- d$x ^ 2
lm(y ~ x2, data = d)
}
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