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This is a major release to signify that this version is associated with a publication (woo!) for this paper in the R Journal. However, this release only represents minor changes, summarised below:
keys_near
related to factorsfeat_diff_summary()
functions to help summarise
diff(). Useful for exploring the time gaps in the index
.
(#100)facet_sample()
now has a default of 3 per plotnear_quantile()
, the tol
argument now
defaults to 0.01.tbl_ts
objects for
keys_near()
- #76pisa
containing a short summary of the
PISA dataset from https://github.com/ropenscilabs/learningtower for
three (of 99) countriesindex_regular()
and
index_summary()
to help identify index variablesfeasts
from dependencies as the functions
required in brolgar
are actually in
fabletools
.nearest_lgl
and nearest_qt_lgl
wages_ts
data.sample_n_obs()
to sample_n_keys()
and sample_frac_keys()
add_k_groups()
to
stratify_keys()
l_<summary>
functions in
favour of the features
approach.l_summarise_fivenum
to l_summarise
,
and have an option to pass a list of functions.l_n_obs()
to n_key_obs()
l_slope()
to key_slope()
monotonic
summaries and
feat_monotonic
l_summarise()
to keys_near()
monotonic
function, which returns TRUE if
increasing or decreasing, and false otherwise.as_tsibble()
and n_keys()
from
`tsibbleworld_heights
gains a continent columnfacet_strata()
to create a random group of
size n_strata
to put the data into (#32). Add support for
along
, and fun
.facet_sample()
to create facetted plots with
a set number of keys inside each facet. (#32).add_
functions now return a tsibble()
(#49).stratify_keys()
didn’t assign an equal
number of keys per strata (#55)wages_ts
dataset to now just be
wages
data, and remove previous tibble()
version of wages
(#39).top_n
argument to keys_near
to provide
control over the number of observations near a stat that are
returned.world_heights
to heights
.n_key_obs()
in favour of using
n_obs()
(#62)filter_n_obs()
in favour of cleaner
workflow with add_n_obs()
(#63)tsibble
.world_heights
dataset, which contains average
male height in centimetres for many countries. #28near_
family of functions to find values near
to a quantile or percentile. So far there are
near_quantile()
, near_middle()
, and
near_between()
(#11).
near_quantile()
Specify some quantile and then find
those values around it (within some specified tolerance).near_middle()
Specify some middle percentile value and
find values within given percentiles.near_between()
Extract percentile values from a given
percentile to another percentile.add_k_groups()
(#20) to randomly split the data
into groups to explore the data.sample_n_obs()
and sample_frac_obs()
(#19) to select a random group of ids.filter_n_obs()
to filter the data by the number of
observations #15var
, in
l_n_obs()
, since it only needs information on the
id
. Also gets a nice 5x speedup with simpler codelongnostic
instead of
lognostic
(#9)l_slope
now returns l_intercept
and
l_slope
instead of intercept
and
slope
.l_slope
now takes bare variable namesl_d1
to l_diff
and added a lag
argument. This makes l_diff
more flexible and the function
more clearly describes its purpose.l_length
to l_n_obs
to more clearly
indicate that this counts the number of observations.longnostic
function to create longnostic
functions to package up reproduced code inside the l_
functions.NEWS.md
file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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