This function requires a tbl_ts object, which can be created with tsibble::as_tsibble(). Under the hood, facet_strata is powered by stratify_keys().

facet_strata(
n_strata = 12,
along = NULL,
fun = mean,
nrow = NULL,
ncol = NULL,
scales = "fixed",
shrink = TRUE,
strip.position = "top"
)

## Arguments

n_strata number of groups to create variable to stratify along. This groups by each key and then takes a summary statistic (by default, the mean). It then arranges by the mean value for each key and assigns the n_strata groups. summary function. Default is mean. Number of rows and columns. Number of rows and columns. Should scales be fixed ("fixed", the default), free ("free"), or free in one dimension ("free_x", "free_y")? If TRUE, will shrink scales to fit output of statistics, not raw data. If FALSE, will be range of raw data before statistical summary. By default, the labels are displayed on the top of the plot. Using strip.position it is possible to place the labels on either of the four sides by setting strip.position = c("top", "bottom", "left", "right")

a ggplot object

## Examples

library(ggplot2)
ggplot(heights,
aes(x = year,
y = height_cm,
group = country)) +
geom_line() +
facet_strata()
ggplot(heights,
aes(x = year,
y = height_cm,
group = country)) +
geom_line() +
facet_wrap(~continent)
ggplot(wages,
aes(x = xp,
y = ln_wages,
group = id)) +
geom_line() +
facet_strata(along = xp_since_ged)
library(dplyr)
wages %>%
key_slope(ln_wages ~ xp) %>%
right_join(wages, ., by = "id") %>%
ggplot(aes(x = xp,
y = ln_wages)) +
geom_line(aes(group = id)) +
geom_smooth(method = "lm") +
facet_strata(along = .slope_xp)#> geom_smooth() using formula 'y ~ x'