I have the following data (cost of a product vs. time) that looks like the following:

annum <- c(1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 
    1914, 1915, 1916, 1917, 1918, 1919)
cost <- c(0.0000,  18.6140,  92.1278, 101.9393, 112.0808, 122.5521, 
    133.3532, 144.4843, 244.5052, 275.6068, 295.2592, 317.3145, 
    339.6527, 362.3537, 377.7775, 402.8443, 437.5539)

mydata <- as.data.frame(cbind(annum, cost))

g <- ggplot(mydata, aes(x = annum, y = cost))
g <- g   geom_point()
g <- g   scale_y_continuous(labels=scales::dollar_format())
g

CodePudding user response：

Here is another solution with package strucchange but without creating a time series first.

library(strucchange)

# first get a segment size as a fraction 
# of the number of observations
n <- nrow(mydata)
segmts <- 3
h <- segmts/n

# now estimate the breakpoints
b <- breakpoints(cost ~ annum, h = h, breaks = (segmts - 1L), data = mydata)
bp <- mydata[b$breakpoints, "annum"]

# create a grouping variable for `ggplot`
# each group is a segment
bp <- c(bp, Inf)
mydata$grp <- findInterval(mydata$annum, bp, left.open = TRUE)

# plot the linear regressions
g   geom_smooth(
  mapping = aes(group = grp),
  method = "lm",
  formula = y ~ x,
  se = FALSE
)