This package adds some extra functionality and plots to the
xpose
framework. This
includes some plots that have been missing in translation from
xpose4
, but also some
useful features that truly extend the capabilities of what can be done
with xpose
.
There are a few bugfixes here and functionality which could easily be
suggested as pull requests to the parent package. Given the size and
broad use of xpose
, it appears even minor pull requests take some time
to implement. As such, this package implements those features directly
and if at any point in the future these are added (perhaps in a better
state) to the parent package, they will be deprecated if this package is
in active use.
For those wondering, conflicted
is
used to manage bugfix conflicts, so users should be comfortable loading
packages in any order.
This package is currently only available here, but submission to CRAN is planned soon.
The typical github installation will work.
devtools::install_github("jprybylski/xpose.xtras")
The grandparent package, xpose4
, has a nice collection of figures and
documentation that is referred to as a
“bestiary”. The
documentation site for this package serves as a complete bestiary, but
see the uncommented examples below as a sort of menagerie. There is no
assumption that these examples are self-explanatory, but hopefully users
familiar with xpose
will recognize the new (and renewed) tools made
available by this package.
described <- xpdb_x %>%
set_var_labels(AGE="Age", MED1 = "Digoxin", .problem = 1) %>%
set_var_units(AGE="yrs") %>%
set_var_levels(SEX=lvl_sex(), MED1 = lvl_bin())
eta_vs_contcov(described,etavar=ETA1, quiet=TRUE)
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
eta_vs_catcov(described,etavar=ETA1, quiet=TRUE)
pheno_set %>%
focus_qapply(backfill_iofv) %>%
dofv_vs_id(run6, run9, quiet = TRUE)
pkpd_m3 %>%
set_var_types(catdv=BLQ,dvprobs=LIKE) %>%
set_dv_probs(1, 1~LIKE, .dv_var = BLQ) %>%
set_var_levels(1, BLQ = lvl_bin()) %>%
catdv_vs_dvprobs(quiet=TRUE)
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
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