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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.

Usage

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

Arguments

.data

data.frame to explore

n_strata

number of groups to create

along

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.

fun

summary function. Default is mean.

...

extra arguments

Value

data.frame with column, .strata containing n_strata groups

Examples

library(ggplot2)
library(brolgar)

heights %>%
  sample_frac_keys(size = 0.1) %>%
  stratify_keys(10) %>%
 ggplot(aes(x = height_cm,
            y = year,
            group = country)) + 
 geom_line() + 
 facet_wrap(~.strata)

 
 # now facet along some feature
library(dplyr)
 heights %>%
key_slope(height_cm ~ year) %>%
  right_join(heights, ., by = "country") %>%
  stratify_keys(n_strata = 12,
                along = .slope_year,
                fun = median) %>%
  ggplot(aes(x = year,
             y = height_cm,
             group = country)) + 
  geom_line() + 
  facet_wrap(~.strata)



heights %>%
  stratify_keys(n_strata = 12,
                along = height_cm) %>%
  ggplot(aes(x = year,
             y = height_cm,
             group = country)) + 
  geom_line() + 
  facet_wrap(~.strata)