To look at as much of the raw data as possible, it can be helpful to stratify the data into groups for plotting. You can stratify the keys using the stratify_keys() function, which adds the column, .strata. This allows the user to create facetted plots showing a more of the raw data.

stratify_keys(.data, n_strata, along = NULL, fun = mean, ...)

## Arguments

.data data.frame to explore 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. extra arguments

## Value

data.frame with column, .strata containing n_strata groups

## Examples

library(ggplot2)
library(brolgar)

wages %>%
sample_frac_keys(size = 0.1) %>%
stratify_keys(10) %>%
ggplot(aes(x = ln_wages,
y = xp,
group = id)) +
geom_line() +
facet_wrap(~.strata)
# now facet along some feature

library(dplyr)
wages %>%
key_slope(ln_wages ~ xp) %>%
right_join(wages, ., by = "id") %>%
stratify_keys(n_strata = 12,
along = .slope_xp,
fun = median) %>%
ggplot(aes(x = xp,
y = ln_wages,
group = id)) +
geom_line() +
facet_wrap(~.strata)

wages %>%
stratify_keys(n_strata = 12,
along = unemploy_rate) %>%
ggplot(aes(x = xp,
y = ln_wages,
group = id)) +
geom_line() +
facet_wrap(~.strata)