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Data

Datasets that come with brolgar

wages
Wages data from National Longitudinal Survey of Youth (NLSY)
heights
World Height Data
pisa
Student data from 2000-2018 PISA OECD data

ggplot helpers

ggplot2 functions to help explore your data

facet_sample()
Facet data into groups to facilitate exploration
facet_strata()
Facet data into groups to facilitate exploration

Sampling helpers

Functions to help sampling series from the data

sample_n_keys() sample_frac_keys()
Sample a number or fraction of keys to explore
stratify_keys()
Stratify the keys into groups to facilitate exploration

Features

Identify features in your data:

Exploratory tools (Experimental)

Tools to help with exploratory modelling of your data

key_slope() add_key_slope() add_key_slope.default()
Fit linear model for each key
keys_near()
Return keys nearest to a given statistics or summary.
keys_near(<data.frame>)
Return keys nearest to a given statistics or summary.
keys_near(<tbl_ts>)
Return keys nearest to a given statistics or summary.

Find observations near a value

Identify observations near some summary value

near_between()
Return x percent to y percent of values
near_middle()
Return the middle x percent of values
near_quantile()
Which values are nearest to any given quantiles
nearest_lgl() nearest_qt_lgl()
Is x nearest to y?

Summaries

Functions that are used to power the summary functions in brolgar

Helpers

Helper functions to assist summarising data

n_obs()
Return the number of observations
add_n_obs()
Add the number of observations for each key in a tsibble
reexports features features_at features_if features_all as_tsibble n_keys
Objects exported from other packages
increasing() decreasing() unvarying() monotonic()
Are values monotonic? Always increasing, decreasing, or unvarying?
index_regular() index_summary()
Index summaries