Stratify the keys into groups to facilitate exploration

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)